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Plug-and-play pixel super-resolution phase retrieval for digital holography

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Abstract

In order to increase signal-to-noise ratio in optical imaging, most detectors sacrifice resolution to increase pixel size in a confined area, which impedes further development of high throughput holographic imaging. Although the pixel super-resolution technique (PSR) enables resolution enhancement, it suffers from the trade-off between reconstruction quality and super-resolution ratio. In this work, we report a high-fidelity PSR phase retrieval method with plug-and-play optimization, termed PNP-PSR. It decomposes PSR reconstruction into independent sub-problems based on generalized alternating projection framework. An alternating projection operator and an enhancing neural network are employed to tackle the measurement fidelity and statistical prior regularization, respectively. PNP-PSR incorporates the advantages of individual operators, achieving both high efficiency and noise robustness. Extensive experiments show that PNP-PSR outperforms the existing techniques in both resolution enhancement and noise suppression.

© 2022 Optica Publishing Group

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Corrections

18 May 2022: A typographical correction was made to the author listing.


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Supplementary Material (1)

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Supplement 1       Supplemental Document

Data availability

The code for the PNP-PSR algorithm is available in Ref. [20]. Experimental data underlying the results presented in this paper may be obtained from the authors upon reasonable request.

20. BIT-Computational Sensing Lab, “Plug-and-play pixel super-resolutution phase retrieval,” https://github.com/bianlab/psr (2022).

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