Abstract

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

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  1. D.A. Skoog. Principles of Instrumental Analysis. Philadelphia: Saunders College Publishing, 1985.
  2. A.K. Arora, V. Umadevi. “Instrumental Distortions of Raman Lines”. Appl. Spectrosc. 1982. 36(4): 424-427. doi:.
    [Crossref]
  3. K. Tanabe, J. Hiraishi. “Correction of Finite Slit Width Effects on Raman Line Widths”. Spectrochim. Acta Part A. 1980. 36(4): 341-344. doi:.
    [Crossref]
  4. S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
    [Crossref]
  5. C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
    [Crossref]
  6. H. Abdi, L.J. Williams. “Principal Component Analysis”. WIREs Comp. Stat. 2010. 2(4): 433-459. doi:.
    [Crossref]
  7. S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
    [Crossref]
  8. I. Noda. “Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy”. Appl. Spectrosc. 1993. 47(9): 1329-1336. doi:.
    [Crossref]
  9. I. Noda. “Techniques Useful in Two-Dimensional Correlation and Codistribution Spectroscopy (2D-COS and 2DCDS) analyses”. J. Mol. Struct. 2016. 1124: 29-41. doi:.
    [Crossref]
  10. Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
    [Crossref]
  11. Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
    [Crossref]
  12. Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
    [Crossref]
  13. H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
    [Crossref]
  14. S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
    [Crossref]
  15. I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
    [Crossref]
  16. X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
    [Crossref]
  17. P. Matousek, M.D. Morris. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields. Heidelberg: Springer, 2010. doi:.
  18. D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
    [Crossref]
  19. D.G. Cameron, D.J. Moffatt. “A Generalized Approach to Derivative Spectroscopy”. Appl. Spectrosc. 1987. 41(4): 539-544. doi:.
    [Crossref]
  20. M.A. Czarnecki. “Resolution Enhancement in Second-Derivative Spectra”. Appl. Spectrosc. 2015. 69(1): 67-74. doi:.
    [Crossref]
  21. M. Hegland, R.S. Anderssen. “Resolution Enhancement of Spectra Using Differentiation”. Inverse Probl. 2005. 21(3): 915-934. doi:.
    [Crossref]
  22. W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
    [Crossref]
  23. I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
    [Crossref]
  24. M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
    [Crossref]
  25. J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
    [Crossref]
  26. A.F. Fell. “Biomedical Applications of Derivative Spectroscopy”. TrAC, Trends Anal. Chem. 1983. 2(3): 63-66. doi:.
    [Crossref]
  27. T.C. O'Haver, A.F. Fell, G. Smith, et al. “Derivative Spectroscopy and Its Applications in Analysis”. Anal. Proc. 1982. 19(1): 22-46. doi:.
    [Crossref]
  28. B.P. Asthana, W. Kiefer. “Deconvolution of the Lorentzian Linewidth and Determination of Fraction Lorentzian Character from the Observed Profile of a Raman Line by a Comparison Technique”. Appl. Spectrosc. 1982. 36(3): 250-257. doi:.
    [Crossref]
  29. H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
    [Crossref]
  30. Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
    [Crossref]
  31. E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
    [Crossref]
  32. S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
    [Crossref]
  33. D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
    [Crossref]
  34. H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
    [Crossref]
  35. T. O'Haver. “A Pragmatic* Introduction to Signal Processing with Applications in Scientific. Measurement”. 2021. http://terpconnect.umd.edu/∼toh/spectrum/TOC.html [accessed Oct 20 2021].
  36. J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
    [Crossref]
  37. S.-H. Wang, P.R. Griffiths. “Resolution Enhancement of Diffuse Reflectance I.R. Spectra of Coals by Fourier Self-Deconvolution: 1. C–H Stretching and Bending Modes”. Fuel. 1985. 64(2): 229-236. doi:.
    [Crossref]
  38. X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
    [Crossref]
  39. D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
    [Crossref]
  40. H. Liu, Z. Zhang, S. Liu, et al. “Richardson–Lucy Blind Deconvolution of Spectroscopic Data With Wavelet Regularization”. Appl. Opt. 2015. 54(7): 1770. doi:.
    [Crossref]
  41. P.A. Jansson, R.H. Hunt, E.K. Plyler. “Resolution Enhancement of Spectra*”. J. Opt. Soc. Am. 1970. 60(5): 596-599. doi:.
    [Crossref]
  42. A.M. Lacapmesure, S. Martínez, O.E. Martínez. “A New Objective Function for Super-Resolution Deconvolution of Microscopy Images by Means of a Genetic Algorithm”. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM, 2020. Pp. 271-272. doi:.
  43. A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.
  44. M. Haris, G. Shakhnarovich, N. Ukita. “Deep Back-Projection Networks for Super-Resolution”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah: IEEE:June 18-22, 2018. Pp. 1664-1673. doi:10.1109/CVPR.2018.00179.
  45. N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
    [Crossref]
  46. W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
    [Crossref]
  47. X. Yuan, R.A. Mayanovic. “An Empirical Study on Raman Peak Fitting and Its Application to Raman Quantitative Research”. Appl. Spectrosc. 2017. 71(10): 2325-2338. doi:.
    [Crossref]
  48. A. Sadat, I.J. Joye. “Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins”. Appl. Sci. 2020. 10(17): 5918. doi:.
    [Crossref]
  49. M. Wojdyr, “Fityk: A General-Purpose Peak Fitting Program”. J. Appl. Crystallogr. 2010. 43(5): 1126-1128. doi:.
    [Crossref]
  50. L. Chen, M. Garland. “Computationally Efficient Curve-Fitting Procedure for Large Two-Dimensional Experimental Infrared Spectroscopic Arrays Using the Pearson VII Model”. Appl. Spectrosc. 2003. 57(3): 331-337. doi:.
    [Crossref]
  51. G. Schulze, A. Jirasek, M.M.L. Yu, et al. “Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation”. Appl. Spectrosc. 2005. 59(5): 545-574. doi:.
    [Crossref]
  52. C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
    [Crossref]
  53. S. Rangan, H.G. Schulze, M.Z. Vardaki, et al. “Applications of Raman Spectroscopy in the Development of Cell Therapies: State of the Art and Future Perspectives”. Analyst. 2020. 145(6): 2070-2105. doi:.
    [Crossref]
  54. H.G. Schulze, S. Rangan, J.M. Piret, et al. “Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples”. Appl. Spectrosc. 2018. 72(9): 1322-1340. doi:.
    [Crossref]
  55. R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
    [Crossref]
  56. H.G. Schulze, R.B. Foist, K. Okuda, et al. “A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra”. Appl. Spectrosc. 2012. 66(7): 757-764. doi:.
    [Crossref]
  57. H.G. Schulze, R.F.B. Turner. “A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra”. Appl. Spectrosc. 2014. 68(2): 185-191. doi:.
    [Crossref]
  58. H.G. Schulze, S. Rangan, J.M. Piret, et al. “EXPRESS: Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High Quality Procedure”. Appl. Spectrosc. 2019. 73(1): 47-58. doi:.
    [Crossref]
  59. F.J. Bartoli, T.A. Litovitz. “Analysis of Orientational Broadening of Raman Line Shapes”. J. Chem. Phys. 1972. 56(1): 404-412. doi:.
    [Crossref]
  60. P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
    [Crossref]
  61. A. Jirasek, G. Schulze, M.W. Blades, R.F.B. Turner. “Revealing System Dynamics Through Decomposition of the Perturbation Domain in Two-Dimensional Correlation Spectroscopy”. Appl. Spectrosc. 2003. 57(12): 1551-1560. doi:.
    [Crossref]
  62. G. Schulze, A. Jirasek, M.W. Blades, R.F.B. Turner. “Identification and Interpretation of Generalized Two-Dimensional Correlation Spectroscopy Features Through Decomposition of the Perturbation Domain”. Appl. Spectrosc. 2003. 57(12): 1561-1574. doi:.
    [Crossref]
  63. H.G. Schulze, C.G. Atkins, D.V. Devine, et al. “The fully Automated Decomposition of Raman Spectra into Individual Pearson's Type VII Distributions Applied to Biological and Biomedical Samples”. Appl. Spectrosc. 2015. 69(1): 26-36. doi:.
    [Crossref]

2020 (5)

C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
[Crossref]

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

A. Sadat, I.J. Joye. “Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins”. Appl. Sci. 2020. 10(17): 5918. doi:.
[Crossref]

C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
[Crossref]

S. Rangan, H.G. Schulze, M.Z. Vardaki, et al. “Applications of Raman Spectroscopy in the Development of Cell Therapies: State of the Art and Future Perspectives”. Analyst. 2020. 145(6): 2070-2105. doi:.
[Crossref]

2019 (7)

H.G. Schulze, S. Rangan, J.M. Piret, et al. “EXPRESS: Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High Quality Procedure”. Appl. Spectrosc. 2019. 73(1): 47-58. doi:.
[Crossref]

W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
[Crossref]

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
[Crossref]

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
[Crossref]

S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
[Crossref]

2018 (5)

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples”. Appl. Spectrosc. 2018. 72(9): 1322-1340. doi:.
[Crossref]

2017 (3)

X. Yuan, R.A. Mayanovic. “An Empirical Study on Raman Peak Fitting and Its Application to Raman Quantitative Research”. Appl. Spectrosc. 2017. 71(10): 2325-2338. doi:.
[Crossref]

D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
[Crossref]

X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
[Crossref]

2016 (4)

I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
[Crossref]

I. Noda. “Techniques Useful in Two-Dimensional Correlation and Codistribution Spectroscopy (2D-COS and 2DCDS) analyses”. J. Mol. Struct. 2016. 1124: 29-41. doi:.
[Crossref]

I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
[Crossref]

N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
[Crossref]

2015 (3)

2014 (1)

2013 (4)

H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
[Crossref]

W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
[Crossref]

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

2012 (1)

2011 (1)

S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
[Crossref]

2010 (2)

H. Abdi, L.J. Williams. “Principal Component Analysis”. WIREs Comp. Stat. 2010. 2(4): 433-459. doi:.
[Crossref]

M. Wojdyr, “Fityk: A General-Purpose Peak Fitting Program”. J. Appl. Crystallogr. 2010. 43(5): 1126-1128. doi:.
[Crossref]

2007 (1)

E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
[Crossref]

2005 (2)

2003 (3)

1996 (1)

R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
[Crossref]

1995 (1)

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

1993 (1)

1987 (2)

D.G. Cameron, D.J. Moffatt. “A Generalized Approach to Derivative Spectroscopy”. Appl. Spectrosc. 1987. 41(4): 539-544. doi:.
[Crossref]

S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
[Crossref]

1985 (1)

S.-H. Wang, P.R. Griffiths. “Resolution Enhancement of Diffuse Reflectance I.R. Spectra of Coals by Fourier Self-Deconvolution: 1. C–H Stretching and Bending Modes”. Fuel. 1985. 64(2): 229-236. doi:.
[Crossref]

1983 (1)

A.F. Fell. “Biomedical Applications of Derivative Spectroscopy”. TrAC, Trends Anal. Chem. 1983. 2(3): 63-66. doi:.
[Crossref]

1982 (3)

1981 (1)

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

1980 (1)

K. Tanabe, J. Hiraishi. “Correction of Finite Slit Width Effects on Raman Line Widths”. Spectrochim. Acta Part A. 1980. 36(4): 341-344. doi:.
[Crossref]

1972 (2)

F.J. Bartoli, T.A. Litovitz. “Analysis of Orientational Broadening of Raman Line Shapes”. J. Chem. Phys. 1972. 56(1): 404-412. doi:.
[Crossref]

P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
[Crossref]

1970 (1)

Abdi, H.

H. Abdi, L.J. Williams. “Principal Component Analysis”. WIREs Comp. Stat. 2010. 2(4): 433-459. doi:.
[Crossref]

Adler Berke, B.

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

Anderssen, R.S.

M. Hegland, R.S. Anderssen. “Resolution Enhancement of Spectra Using Differentiation”. Inverse Probl. 2005. 21(3): 915-934. doi:.
[Crossref]

Arora, A.K.

Asthana, B.P.

Atkins, C.G.

H.G. Schulze, C.G. Atkins, D.V. Devine, et al. “The fully Automated Decomposition of Raman Spectra into Individual Pearson's Type VII Distributions Applied to Biological and Biomedical Samples”. Appl. Spectrosc. 2015. 69(1): 26-36. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
[Crossref]

Bartoli, F.J.

F.J. Bartoli, T.A. Litovitz. “Analysis of Orientational Broadening of Raman Line Shapes”. J. Chem. Phys. 1972. 56(1): 404-412. doi:.
[Crossref]

Blackwell, J.

P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
[Crossref]

Blades, M.W.

Brinicombe, A.M.

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

Cameron, D.G.

D.G. Cameron, D.J. Moffatt. “A Generalized Approach to Derivative Spectroscopy”. Appl. Spectrosc. 1987. 41(4): 539-544. doi:.
[Crossref]

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

Chen, J.

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

Chen, L.

Chu, K.

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

Czarnecki, M.A.

De Figueiredo, L.H.

R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
[Crossref]

Deng, L.

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Devine, D.V.

Donati, L.

D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
[Crossref]

Durand, F.

A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.

Esbensen, K.

S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
[Crossref]

Fang, H.

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

Fell, A.F.

A.F. Fell. “Biomedical Applications of Derivative Spectroscopy”. TrAC, Trends Anal. Chem. 1983. 2(3): 63-66. doi:.
[Crossref]

T.C. O'Haver, A.F. Fell, G. Smith, et al. “Derivative Spectroscopy and Its Applications in Analysis”. Anal. Proc. 1982. 19(1): 22-46. doi:.
[Crossref]

Filho, W.C

R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
[Crossref]

Fish, D.A.

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

Fityk, "

M. Wojdyr, “Fityk: A General-Purpose Peak Fitting Program”. J. Appl. Crystallogr. 2010. 43(5): 1126-1128. doi:.
[Crossref]

Foist, R.B.

Freeman, W.T.

A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.

Garland, M.

Geladi, P.

S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
[Crossref]

Ghaffari, M.

C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
[Crossref]

Griffiths, P.R.

S.-H. Wang, P.R. Griffiths. “Resolution Enhancement of Diffuse Reflectance I.R. Spectra of Coals by Fourier Self-Deconvolution: 1. C–H Stretching and Bending Modes”. Fuel. 1985. 64(2): 229-236. doi:.
[Crossref]

Gritti, F.

M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
[Crossref]

Haris, M.

M. Haris, G. Shakhnarovich, N. Ukita. “Deep Back-Projection Networks for Super-Resolution”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah: IEEE:June 18-22, 2018. Pp. 1664-1673. doi:10.1109/CVPR.2018.00179.

Hegland, M.

M. Hegland, R.S. Anderssen. “Resolution Enhancement of Spectra Using Differentiation”. Inverse Probl. 2005. 21(3): 915-934. doi:.
[Crossref]

Hiraishi, J.

K. Tanabe, J. Hiraishi. “Correction of Finite Slit Width Effects on Raman Line Widths”. Spectrochim. Acta Part A. 1980. 36(4): 341-344. doi:.
[Crossref]

Hsu, H.-C.

W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
[Crossref]

Hsu, K.-J.

I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
[Crossref]

Hu, C.

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

Hunt, R.H.

Ierusalimschy, R.

R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
[Crossref]

Jansson, P.A.

Javed, S.G.

N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
[Crossref]

Jin, S.

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

Jirasek, A.

Joye, I.J.

A. Sadat, I.J. Joye. “Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins”. Appl. Sci. 2020. 10(17): 5918. doi:.
[Crossref]

Jung, Y.M.

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Kauppinen, J.K.

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

Kausar, N.

N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
[Crossref]

Kiefer, W.

Klann, E.

E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
[Crossref]

Koenig, J.L.

P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
[Crossref]

Konorov, S.O.

H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
[Crossref]

Krafft, C.

I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
[Crossref]

Kuhn, M.

E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
[Crossref]

Kuo, W.-C.

W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
[Crossref]

Lacapmesure, A.M.

A.M. Lacapmesure, S. Martínez, O.E. Martínez. “A New Objective Function for Super-Resolution Deconvolution of Microscopy Images by Means of a Genetic Algorithm”. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM, 2020. Pp. 271-272. doi:.

Levin, A.

A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.

Li, H.

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Li, Y.

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Litovitz, T.A.

F.J. Bartoli, T.A. Litovitz. “Analysis of Orientational Broadening of Raman Line Shapes”. J. Chem. Phys. 1972. 56(1): 404-412. doi:.
[Crossref]

Liu, H.

H. Liu, Z. Zhang, S. Liu, et al. “Richardson–Lucy Blind Deconvolution of Spectroscopic Data With Wavelet Regularization”. Appl. Opt. 2015. 54(7): 1770. doi:.
[Crossref]

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

Liu, S.

Lorenz, D.A.

E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
[Crossref]

Ma, X.

X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
[Crossref]

Majid, A.

N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
[Crossref]

Malka, D.

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

Mantsch, H.H.

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

Martinez, O.E.

S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
[Crossref]

Martínez, O.E.

A.M. Lacapmesure, S. Martínez, O.E. Martínez. “A New Objective Function for Super-Resolution Deconvolution of Microscopy Images by Means of a Genetic Algorithm”. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM, 2020. Pp. 271-272. doi:.

Martínez, S.

S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
[Crossref]

A.M. Lacapmesure, S. Martínez, O.E. Martínez. “A New Objective Function for Super-Resolution Deconvolution of Microscopy Images by Means of a Genetic Algorithm”. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM, 2020. Pp. 271-272. doi:.

Matousek, P.

P. Matousek, M.D. Morris. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields. Heidelberg: Springer, 2010. doi:.

Mayanovic, R.A.

Moffatt, D.J.

D.G. Cameron, D.J. Moffatt. “A Generalized Approach to Derivative Spectroscopy”. Appl. Spectrosc. 1987. 41(4): 539-544. doi:.
[Crossref]

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

Morris, M.D.

P. Matousek, M.D. Morris. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields. Heidelberg: Springer, 2010. doi:.

Noda, I.

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

I. Noda. “Techniques Useful in Two-Dimensional Correlation and Codistribution Spectroscopy (2D-COS and 2DCDS) analyses”. J. Mol. Struct. 2016. 1124: 29-41. doi:.
[Crossref]

I. Noda. “Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy”. Appl. Spectrosc. 1993. 47(9): 1329-1336. doi:.
[Crossref]

O'Haver, T.C.

M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
[Crossref]

T.C. O'Haver, A.F. Fell, G. Smith, et al. “Derivative Spectroscopy and Its Applications in Analysis”. Anal. Proc. 1982. 19(1): 22-46. doi:.
[Crossref]

Okuda, K.

O'Mahony, J.

C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
[Crossref]

Ozaki, Y.

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Park, Y.

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

Pike, E.R.

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

Piret, J.M.

H.G. Schulze, S. Rangan, J.M. Piret, et al. “EXPRESS: Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High Quality Procedure”. Appl. Spectrosc. 2019. 73(1): 47-58. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples”. Appl. Spectrosc. 2018. 72(9): 1322-1340. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
[Crossref]

H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
[Crossref]

Plyler, E.K.

Popp, J.

I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
[Crossref]

Rafferty, C.

C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
[Crossref]

Rangan, S.

Rea, R.

C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
[Crossref]

Ruckebusch, C.

C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
[Crossref]

Sadat, A.

A. Sadat, I.J. Joye. “Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins”. Appl. Sci. 2020. 10(17): 5918. doi:.
[Crossref]

Sage, D.

D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
[Crossref]

Schie, I.W.

I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
[Crossref]

Schulze, G.

Schulze, H.G.

S. Rangan, H.G. Schulze, M.Z. Vardaki, et al. “Applications of Raman Spectroscopy in the Development of Cell Therapies: State of the Art and Future Perspectives”. Analyst. 2020. 145(6): 2070-2105. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “EXPRESS: Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High Quality Procedure”. Appl. Spectrosc. 2019. 73(1): 47-58. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples”. Appl. Spectrosc. 2018. 72(9): 1322-1340. doi:.
[Crossref]

H.G. Schulze, C.G. Atkins, D.V. Devine, et al. “The fully Automated Decomposition of Raman Spectra into Individual Pearson's Type VII Distributions Applied to Biological and Biomedical Samples”. Appl. Spectrosc. 2015. 69(1): 26-36. doi:.
[Crossref]

H.G. Schulze, R.F.B. Turner. “A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra”. Appl. Spectrosc. 2014. 68(2): 185-191. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
[Crossref]

H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
[Crossref]

H.G. Schulze, R.B. Foist, K. Okuda, et al. “A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra”. Appl. Spectrosc. 2012. 66(7): 757-764. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
[Crossref]

Shakhnarovich, G.

M. Haris, G. Shakhnarovich, N. Ukita. “Deep Back-Projection Networks for Super-Resolution”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah: IEEE:June 18-22, 2018. Pp. 1664-1673. doi:10.1109/CVPR.2018.00179.

Shen, P.-T.

I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
[Crossref]

Shih, Y.-T.

W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
[Crossref]

Skoog, D.A.

D.A. Skoog. Principles of Instrumental Analysis. Philadelphia: Saunders College Publishing, 1985.

Smith, G.

T.C. O'Haver, A.F. Fell, G. Smith, et al. “Derivative Spectroscopy and Its Applications in Analysis”. Anal. Proc. 1982. 19(1): 22-46. doi:.
[Crossref]

Smith, Z.J.

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

Soulez, F.

D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
[Crossref]

Su, I.-C.

I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
[Crossref]

Tanabe, K.

K. Tanabe, J. Hiraishi. “Correction of Finite Slit Width Effects on Raman Line Widths”. Spectrochim. Acta Part A. 1980. 36(4): 341-344. doi:.
[Crossref]

Tao, Y.

Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
[Crossref]

Tian, Y.

W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
[Crossref]

Tischler, Y.

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

Toscani, M.

S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
[Crossref]

Turner, R.F.B.

Ukita, N.

M. Haris, G. Shakhnarovich, N. Ukita. “Deep Back-Projection Networks for Super-Resolution”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah: IEEE:June 18-22, 2018. Pp. 1664-1673. doi:10.1109/CVPR.2018.00179.

Umadevi, V.

Vardaki, M.Z.

S. Rangan, H.G. Schulze, M.Z. Vardaki, et al. “Applications of Raman Spectroscopy in the Development of Cell Therapies: State of the Art and Future Perspectives”. Analyst. 2020. 145(6): 2070-2105. doi:.
[Crossref]

Vasko, P.D.

P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
[Crossref]

Vitale, R.

C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
[Crossref]

Wahab, M.F.

M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
[Crossref]

Walker, J.G.

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

Wang, H.

X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
[Crossref]

Wang, S.-H.

S.-H. Wang, P.R. Griffiths. “Resolution Enhancement of Diffuse Reflectance I.R. Spectra of Coals by Fourier Self-Deconvolution: 1. C–H Stretching and Bending Modes”. Fuel. 1985. 64(2): 229-236. doi:.
[Crossref]

Wang, X.

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

Wang, Y.

Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
[Crossref]

X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
[Crossref]

Weiss, Y.

A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.

Williams, L.J.

H. Abdi, L.J. Williams. “Principal Component Analysis”. WIREs Comp. Stat. 2010. 2(4): 433-459. doi:.
[Crossref]

Wojdyr, M.

M. Wojdyr, “Fityk: A General-Purpose Peak Fitting Program”. J. Appl. Crystallogr. 2010. 43(5): 1126-1128. doi:.
[Crossref]

Wold, S.

S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
[Crossref]

Wu, Y.

Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
[Crossref]

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Yan, L.

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

Yang, C.

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

Yang, W.

W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
[Crossref]

Yang, Y.

Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
[Crossref]

Yu, M.M.L.

Yuan, X.

Zalevsky, Z.

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

Zhang, D.

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

Zhang, L.

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
[Crossref]

Zhang, X.

W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
[Crossref]

Zhang, Z.

Zhu, H.

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Zhu, M.

Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
[Crossref]

Anal. Chem. (2)

S.O. Konorov, H.G. Schulze, C.G. Atkins, et al. “Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy”. Anal. Chem. 2011. 83(16): 6254-6258. doi:.
[Crossref]

S.O. Konorov, H.G. Schulze, J.M. Piret, et al. “Raman Microscopy-Based Label Free Determination of the Cell Cycle Phase in Human Embryonic Stem Cells”. Anal. Chem. 2013. 85(19): 8996-9002. doi:.
[Crossref]

Anal. Proc. (1)

T.C. O'Haver, A.F. Fell, G. Smith, et al. “Derivative Spectroscopy and Its Applications in Analysis”. Anal. Proc. 1982. 19(1): 22-46. doi:.
[Crossref]

Analyst (3)

X. Wang, C. Hu, K. Chu, Z.J. Smith. “Low Resolution Raman: The Impact of Spectral Resolution on Limit of Detection and Imaging Speed in Hyperspectral Imaging”. Analyst. 2020. 145(20): 6607-6616. doi:.
[Crossref]

H.G. Schulze, S.O. Konorov, J.M. Piret, et al. “Label-Free Imaging of Mammalian Cell Nucleoli by Raman Microspectroscopy”. Analyst. 2013. 138(12): 3416-3423. doi:.
[Crossref]

S. Rangan, H.G. Schulze, M.Z. Vardaki, et al. “Applications of Raman Spectroscopy in the Development of Cell Therapies: State of the Art and Future Perspectives”. Analyst. 2020. 145(6): 2070-2105. doi:.
[Crossref]

Appl. Opt. (1)

Appl. Sci. (1)

A. Sadat, I.J. Joye. “Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins”. Appl. Sci. 2020. 10(17): 5918. doi:.
[Crossref]

Appl. Spectrosc (1)

J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron. “Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands”. Appl. Spectrosc. 1981. 35(3): 271-276. doi:.
[Crossref]

Appl. Spectrosc. (15)

B.P. Asthana, W. Kiefer. “Deconvolution of the Lorentzian Linewidth and Determination of Fraction Lorentzian Character from the Observed Profile of a Raman Line by a Comparison Technique”. Appl. Spectrosc. 1982. 36(3): 250-257. doi:.
[Crossref]

D.G. Cameron, D.J. Moffatt. “A Generalized Approach to Derivative Spectroscopy”. Appl. Spectrosc. 1987. 41(4): 539-544. doi:.
[Crossref]

M.A. Czarnecki. “Resolution Enhancement in Second-Derivative Spectra”. Appl. Spectrosc. 2015. 69(1): 67-74. doi:.
[Crossref]

A.K. Arora, V. Umadevi. “Instrumental Distortions of Raman Lines”. Appl. Spectrosc. 1982. 36(4): 424-427. doi:.
[Crossref]

I. Noda. “Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy”. Appl. Spectrosc. 1993. 47(9): 1329-1336. doi:.
[Crossref]

L. Chen, M. Garland. “Computationally Efficient Curve-Fitting Procedure for Large Two-Dimensional Experimental Infrared Spectroscopic Arrays Using the Pearson VII Model”. Appl. Spectrosc. 2003. 57(3): 331-337. doi:.
[Crossref]

G. Schulze, A. Jirasek, M.M.L. Yu, et al. “Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation”. Appl. Spectrosc. 2005. 59(5): 545-574. doi:.
[Crossref]

H.G. Schulze, R.B. Foist, K. Okuda, et al. “A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra”. Appl. Spectrosc. 2012. 66(7): 757-764. doi:.
[Crossref]

H.G. Schulze, R.F.B. Turner. “A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra”. Appl. Spectrosc. 2014. 68(2): 185-191. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “EXPRESS: Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High Quality Procedure”. Appl. Spectrosc. 2019. 73(1): 47-58. doi:.
[Crossref]

X. Yuan, R.A. Mayanovic. “An Empirical Study on Raman Peak Fitting and Its Application to Raman Quantitative Research”. Appl. Spectrosc. 2017. 71(10): 2325-2338. doi:.
[Crossref]

H.G. Schulze, S. Rangan, J.M. Piret, et al. “Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples”. Appl. Spectrosc. 2018. 72(9): 1322-1340. doi:.
[Crossref]

A. Jirasek, G. Schulze, M.W. Blades, R.F.B. Turner. “Revealing System Dynamics Through Decomposition of the Perturbation Domain in Two-Dimensional Correlation Spectroscopy”. Appl. Spectrosc. 2003. 57(12): 1551-1560. doi:.
[Crossref]

G. Schulze, A. Jirasek, M.W. Blades, R.F.B. Turner. “Identification and Interpretation of Generalized Two-Dimensional Correlation Spectroscopy Features Through Decomposition of the Perturbation Domain”. Appl. Spectrosc. 2003. 57(12): 1561-1574. doi:.
[Crossref]

H.G. Schulze, C.G. Atkins, D.V. Devine, et al. “The fully Automated Decomposition of Raman Spectra into Individual Pearson's Type VII Distributions Applied to Biological and Biomedical Samples”. Appl. Spectrosc. 2015. 69(1): 26-36. doi:.
[Crossref]

Bioprocess Biosyst. Eng. (1)

C. Rafferty, J. O'Mahony, R. Rea, et al. “Raman Spectroscopic Based Chemometric Models to Support a Dynamic Capacitance Based Cell Culture Feeding Strategy”. Bioprocess Biosyst. Eng. 2020. 43(8): 1415-1429. doi:.
[Crossref]

Carbohydr. Res. (1)

P.D. Vasko, J. Blackwell, J.L. Koenig. “Infrared and Raman Spectroscopy of Carbohydrates: Normal Coordinate Analysis of α-D-Glucose”. Carbohydr. Res. 1972. 23(3): 407-416. doi:.
[Crossref]

Chemom. Intell. Lab. Syst. (1)

S. Wold, K. Esbensen, P. Geladi. “Principal Component Analysis”. Chemom. Intell. Lab. Syst. 1987. 2(1-3): 37-52. doi:.
[Crossref]

Chromatographia (1)

M.F. Wahab, F. Gritti, T.C. O'Haver, et al. “Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks”. Chromatographia. 2019. 82(1): 211-220. doi:.
[Crossref]

Comput. Electr. Eng. (1)

H. Zhu, L. Deng, H. Li, Y. Li. “Deconvolution Methods Based on Convex Regularization for Spectral Resolution Enhancement”. Comput. Electr. Eng. 2018. 70: 959-967. doi:.
[Crossref]

Eng. Appl. Artif. Intell. (1)

N. Kausar, A. Majid, S.G. Javed. “Developing Learning Based Intelligent Fusion for Deblurring Confocal Microscopic Images”. Eng. Appl. Artif. Intell. 2016. 55: 339-352. doi:.
[Crossref]

Fuel (1)

S.-H. Wang, P.R. Griffiths. “Resolution Enhancement of Diffuse Reflectance I.R. Spectra of Coals by Fourier Self-Deconvolution: 1. C–H Stretching and Bending Modes”. Fuel. 1985. 64(2): 229-236. doi:.
[Crossref]

IEEE Trans. Instrum. Meas. (1)

H. Liu, L. Yan, H. Fang, D. Zhang. “Spectral Deconvolution and Feature Extraction with Robust Adaptive Tikhonov Regularization”. IEEE Trans. Instrum. Meas. 2013. 62(2): 315-327. doi:
[Crossref]

IEEE Trans. Multimed. (1)

W. Yang, X. Zhang, Y. Tian, et al. “Deep Learning for Single Image Super-Resolution: A Brief Review”. IEEE Trans. Multimed. 2019. 21(12): 3106-3121. doi:.
[Crossref]

Inverse Probl (2)

M. Hegland, R.S. Anderssen. “Resolution Enhancement of Spectra Using Differentiation”. Inverse Probl. 2005. 21(3): 915-934. doi:.
[Crossref]

E. Klann, M. Kuhn, D.A. Lorenz, et al. “Shrinkage Versus Deconvolution”. Inverse Probl. 2007. 23(5): 2231-2248. doi:.
[Crossref]

J. Appl. Crystallogr. (1)

M. Wojdyr, “Fityk: A General-Purpose Peak Fitting Program”. J. Appl. Crystallogr. 2010. 43(5): 1126-1128. doi:.
[Crossref]

J. Biophotonics. (1)

I.W. Schie, C. Krafft, J. Popp. “Cell Classification with Low-Resolution Raman Spectroscopy (LRRS)”. J. Biophotonics. 2016. 9(10): 994-1000. doi:.
[Crossref]

J. Chem. Phys (1)

F.J. Bartoli, T.A. Litovitz. “Analysis of Orientational Broadening of Raman Line Shapes”. J. Chem. Phys. 1972. 56(1): 404-412. doi:.
[Crossref]

J. Microsc. (1)

S. Martínez, M. Toscani, O.E. Martinez. “Superresolution Method for a Single Wide‐Field Image Deconvolution by Superposition of Point Sources”. J. Microsc. 2019. 275(1): 51-65. doi:.
[Crossref]

J. Mol. Struct. (2)

I. Noda. “Techniques Useful in Two-Dimensional Correlation and Codistribution Spectroscopy (2D-COS and 2DCDS) analyses”. J. Mol. Struct. 2016. 1124: 29-41. doi:.
[Crossref]

Y. Park, S. Jin, I. Noda, Y.M. Jung. “Recent Progresses in Two-Dimensional Correlation Spectroscopy (2D-COS)”. J. Mol. Struct. 2018. 1168: 1-21. doi:.
[Crossref]

J. Opt. (1)

D. Malka, B. Adler Berke, Y. Tischler, Z. Zalevsky. “Improving Raman Spectra of Pure Silicon Using Super-Resolved Method”. J. Opt. 2019. 21(7): 075801. doi:.
[Crossref]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A. (1)

D.A. Fish, J.G. Walker, A.M. Brinicombe, E.R. Pike. “Blind Deconvolution by Means of the Richardson–Lucy Algorithm”. J. Opt. Soc. Am. A. 1995. 12(1): 58-65. doi:.
[Crossref]

Methods (1)

D. Sage, L. Donati, F. Soulez, et al. “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy”. Methods. 2017. 115: 28-41. doi:.
[Crossref]

Opt. Express. (3)

X. Ma, H. Wang, Y. Wang, et al. “Improving the Resolution and the Throughput of Spectrometers by a Digital Projection Slit”. Opt. Express. 2017. 25(19): 23045. doi:.
[Crossref]

W.-C. Kuo, Y.-T. Shih, H.-C. Hsu, et al. “Virtual Spatial Overlap Modulation Microscopy for Resolution Improvement”. Opt. Express. 2013. 21(24): 30007. doi:.
[Crossref]

I.-C. Su, K.-J. Hsu, P.-T. Shen, et al. “3D resolution Enhancement of Deep-Tissue Imaging Based on Virtual Spatial Overlap Modulation Microscopy”. Opt. Express. 2016. 24(15): 16238. doi:.
[Crossref]

Sensors (1)

Y. Yang, M. Zhu, Y. Wang, et al. “Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology”. Sensors. 2019. 19(19): 4076. doi:.
[Crossref]

Softw. Pract. Exp. (1)

R. Ierusalimschy, L.H. De Figueiredo, W.C Filho. “Lua: An Extensible Extension Language”. Softw. Pract. Exp. 1996. 26(6): 635-652. doi:.
[Crossref]

Spectrochim. Acta Part A. (1)

K. Tanabe, J. Hiraishi. “Correction of Finite Slit Width Effects on Raman Line Widths”. Spectrochim. Acta Part A. 1980. 36(4): 341-344. doi:.
[Crossref]

Spectrochim. Acta, Part A. (2)

Y. Wu, L. Zhang, Y.M. Jung, Y. Ozaki. “Two-Dimensional Correlation Spectroscopy in Protein Science, a Summary for Past 20 Years”. Spectrochim. Acta, Part A. 2018. 189: 291-299. doi:.
[Crossref]

Y. Tao, Y. Wu, L. Zhang. “Advancements of Two-Dimensional Correlation Spectroscopy in Protein Researches”. Spectrochim. Acta, Part A. 2018. 197: 185-193. doi:.
[Crossref]

Spectrosc. Lett. (1)

J. Chen, C. Yang, H. Zhu, Y. Li. “Adaptive Signal Enhancement for Overlapped Peaks Based on Weighting Factor Selection”. Spectrosc. Lett. 2019. 52(1): 49-59. doi:.
[Crossref]

TrAC, Trends Anal. Chem. (2)

A.F. Fell. “Biomedical Applications of Derivative Spectroscopy”. TrAC, Trends Anal. Chem. 1983. 2(3): 63-66. doi:.
[Crossref]

C. Ruckebusch, R. Vitale, M. Ghaffari, et al. “Perspective on Essential Information in Multivariate Curve Resolution”. TrAC, Trends Anal. Chem. 2020. 132: 116044. doi:.
[Crossref]

WIREs Comp. Stat. (1)

H. Abdi, L.J. Williams. “Principal Component Analysis”. WIREs Comp. Stat. 2010. 2(4): 433-459. doi:.
[Crossref]

Other (6)

P. Matousek, M.D. Morris. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields. Heidelberg: Springer, 2010. doi:.

D.A. Skoog. Principles of Instrumental Analysis. Philadelphia: Saunders College Publishing, 1985.

T. O'Haver. “A Pragmatic* Introduction to Signal Processing with Applications in Scientific. Measurement”. 2021. http://terpconnect.umd.edu/∼toh/spectrum/TOC.html [accessed Oct 20 2021].

A.M. Lacapmesure, S. Martínez, O.E. Martínez. “A New Objective Function for Super-Resolution Deconvolution of Microscopy Images by Means of a Genetic Algorithm”. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM, 2020. Pp. 271-272. doi:.

A. Levin, Y. Weiss, F. Durand, W.T. Freeman. “Understanding and Evaluating Blind Deconvolution Algorithms”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida: June 20-25, 2009. Pp. 1964-1971. doi:10.1109/CVPR.2009.5206815.

M. Haris, G. Shakhnarovich, N. Ukita. “Deep Back-Projection Networks for Super-Resolution”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah: IEEE:June 18-22, 2018. Pp. 1664-1673. doi:10.1109/CVPR.2018.00179.

Supplementary Material (1)

NameDescription
Supplement 1       sj-pdf-1-asp-10.1177_00037028211061174 – Supplemental Material for Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

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