Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Technique for enhancing the accuracy of the Rayleigh–Sommerfeld convolutional diffraction through the utilization of independent spatial sampling

Not Accessible

Your library or personal account may give you access

Abstract

The Rayleigh–Sommerfeld diffraction integral (RSD) is a rigorous solution that precisely satisfies both Maxwell’s equations and Helmholtz’s equations. It seamlessly integrates Huygens’ principle, providing an accurate description of the coherent light propagation within the entire diffraction field. Therefore, the rapid and precise computation of the RSD is crucial for light transport simulation and optical technology applications based on it. However, the current FFT-based Rayleigh–Sommerfeld integral convolution algorithm (CRSD) exhibits poor performance in the near field, thereby limiting its applicability and impeding further development across various fields. The present study proposes, to our knowledge, a novel approach to enhance the accuracy of the Rayleigh–Sommerfeld convolution algorithm by employing independent sampling techniques in both spatial and frequency domains. The crux of this methodology involves segregating the spatial and frequency domains, followed by autonomous sampling within each domain. The proposed method significantly enhances the accuracy of RSD during the short distance while ensuring computational efficiency.

© 2024 Optica Publishing Group

Full Article  |  PDF Article

Corrections

Wanli Zhao, Jing Lu, Jun Ma, Caojin Yuan, Chenliang Chang, and Rihong Zhu, "Technique for enhancing the accuracy of the Rayleigh–Sommerfeld convolutional diffraction through the utilization of independent spatial sampling: publisher’s note," Opt. Lett. 49, 1811-1811 (2024)
https://opg.optica.org/ol/abstract.cfm?uri=ol-49-7-1811

18 March 2024: Typographical corrections were made to the author affiliation.


More Like This
A flexible numerical calculation method of angular spectrum based on matrix product

Wanli Zhao, Chenlu Wei, Caojin Yuan, Chenliang Chang, Jun Ma, and Rihong Zhu
Opt. Lett. 45(21) 5937-5940 (2020)

Adaptive-sampling angular spectrum method with full utilization of space-bandwidth product

Wenhui Zhang, Hao Zhang, and Guofan Jin
Opt. Lett. 45(16) 4416-4419 (2020)

Is the Rayleigh-Sommerfeld diffraction always an exact reference for high speed diffraction algorithms?

Soheil Mehrabkhani and Thomas Schneider
Opt. Express 25(24) 30229-30240 (2017)

Supplementary Material (1)

NameDescription
Supplement 1       Description of BLFFT improvements

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (4)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (2)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (18)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.