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Universal linear intensity transformations using spatially incoherent diffractive processors
Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is ≥~2...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427714/ https://www.ncbi.nlm.nih.gov/pubmed/37582771 http://dx.doi.org/10.1038/s41377-023-01234-y |
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author | Rahman, Md Sadman Sakib Yang, Xilin Li, Jingxi Bai, Bijie Ozcan, Aydogan |
author_facet | Rahman, Md Sadman Sakib Yang, Xilin Li, Jingxi Bai, Bijie Ozcan, Aydogan |
author_sort | Rahman, Md Sadman Sakib |
collection | PubMed |
description | Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is ≥~2N(i)N(o), where N(i) and N(o) refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function (H) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H(m, n; m′, n′) = |h(m, n; m′, n′)|(2), where h is the spatially coherent point spread function of the same diffractive network, and (m, n) and (m′, n′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2N(i)N(o). We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light. |
format | Online Article Text |
id | pubmed-10427714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104277142023-08-17 Universal linear intensity transformations using spatially incoherent diffractive processors Rahman, Md Sadman Sakib Yang, Xilin Li, Jingxi Bai, Bijie Ozcan, Aydogan Light Sci Appl Article Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is ≥~2N(i)N(o), where N(i) and N(o) refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function (H) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H(m, n; m′, n′) = |h(m, n; m′, n′)|(2), where h is the spatially coherent point spread function of the same diffractive network, and (m, n) and (m′, n′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2N(i)N(o). We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light. Nature Publishing Group UK 2023-08-15 /pmc/articles/PMC10427714/ /pubmed/37582771 http://dx.doi.org/10.1038/s41377-023-01234-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rahman, Md Sadman Sakib Yang, Xilin Li, Jingxi Bai, Bijie Ozcan, Aydogan Universal linear intensity transformations using spatially incoherent diffractive processors |
title | Universal linear intensity transformations using spatially incoherent diffractive processors |
title_full | Universal linear intensity transformations using spatially incoherent diffractive processors |
title_fullStr | Universal linear intensity transformations using spatially incoherent diffractive processors |
title_full_unstemmed | Universal linear intensity transformations using spatially incoherent diffractive processors |
title_short | Universal linear intensity transformations using spatially incoherent diffractive processors |
title_sort | universal linear intensity transformations using spatially incoherent diffractive processors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427714/ https://www.ncbi.nlm.nih.gov/pubmed/37582771 http://dx.doi.org/10.1038/s41377-023-01234-y |
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