Compressive single-pixel imaging via a wavelength-multiplexed spatially incoherent diffractive optical processor

Fuente: arXiv

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Bibliographic Details
Main Authors:	Wang, Xiao, Wu, Yiyang, Wang, Yuntian, Rahman, Md Sadman Sakib, Costa, Paloma Casteleiro, Ma, Guangdong, Chen, Shiqi, Li, Yuzhu, Li, Jingxi, Isil, Cagatay, Ozcan, Aydogan
Format:	Preprint
Published:	2026
Subjects:	Optics Neural and Evolutionary Computing
Online Access:	Acceder al recurso
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_version_	1866908905837166592
author	Wang, Xiao Wu, Yiyang Wang, Yuntian Rahman, Md Sadman Sakib Costa, Paloma Casteleiro Ma, Guangdong Chen, Shiqi Li, Yuzhu Li, Jingxi Isil, Cagatay Ozcan, Aydogan
author_facet	Wang, Xiao Wu, Yiyang Wang, Yuntian Rahman, Md Sadman Sakib Costa, Paloma Casteleiro Ma, Guangdong Chen, Shiqi Li, Yuzhu Li, Jingxi Isil, Cagatay Ozcan, Aydogan
contents	Despite offering high sensitivity, a high signal-to-noise ratio, and a broad spectral range, single-pixel imaging (SPI) is limited by low measurement efficiency and long data-acquisition times. To address this, we propose a wavelength-multiplexed, spatially incoherent diffractive optical processor combined with a compact/shallow digital artificial neural network (ANN) to implement compressive SPI. Specifically, we model the bucket detection process in conventional SPI as a linear intensity transformation with spatially and spectrally varying point-spread functions. This transformation matrix is treated as a learnable parameter and jointly optimized with a shallow digital ANN composed of 2 hidden nonlinear layers. The wavelength-multiplexed diffractive processor is then configured via data-free optimization to approximate this pre-trained transformation matrix; after this optimization, the diffractive processor remains static/fixed. Upon multi-wavelength illumination and diffractive modulation, the target spatial information of the input object is spectrally encoded. A single-pixel detector captures the output spectral power at each illumination band, which is then rapidly decoded by the jointly trained digital ANN to reconstruct the input image. In addition to our numerical analyses demonstrating the feasibility of this approach, we experimentally validated its proof-of-concept using an array of light-emitting diodes (LEDs). Overall, this work demonstrates a computational imaging framework for compressive SPI that can be useful in applications such as biomedical imaging, autonomous devices, and remote sensing.
format	Preprint
id	arxiv_https___arxiv_org_abs_2603_21456
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Compressive single-pixel imaging via a wavelength-multiplexed spatially incoherent diffractive optical processor Wang, Xiao Wu, Yiyang Wang, Yuntian Rahman, Md Sadman Sakib Costa, Paloma Casteleiro Ma, Guangdong Chen, Shiqi Li, Yuzhu Li, Jingxi Isil, Cagatay Ozcan, Aydogan Optics Neural and Evolutionary Computing Despite offering high sensitivity, a high signal-to-noise ratio, and a broad spectral range, single-pixel imaging (SPI) is limited by low measurement efficiency and long data-acquisition times. To address this, we propose a wavelength-multiplexed, spatially incoherent diffractive optical processor combined with a compact/shallow digital artificial neural network (ANN) to implement compressive SPI. Specifically, we model the bucket detection process in conventional SPI as a linear intensity transformation with spatially and spectrally varying point-spread functions. This transformation matrix is treated as a learnable parameter and jointly optimized with a shallow digital ANN composed of 2 hidden nonlinear layers. The wavelength-multiplexed diffractive processor is then configured via data-free optimization to approximate this pre-trained transformation matrix; after this optimization, the diffractive processor remains static/fixed. Upon multi-wavelength illumination and diffractive modulation, the target spatial information of the input object is spectrally encoded. A single-pixel detector captures the output spectral power at each illumination band, which is then rapidly decoded by the jointly trained digital ANN to reconstruct the input image. In addition to our numerical analyses demonstrating the feasibility of this approach, we experimentally validated its proof-of-concept using an array of light-emitting diodes (LEDs). Overall, this work demonstrates a computational imaging framework for compressive SPI that can be useful in applications such as biomedical imaging, autonomous devices, and remote sensing.
title	Compressive single-pixel imaging via a wavelength-multiplexed spatially incoherent diffractive optical processor
topic	Optics Neural and Evolutionary Computing
url	https://arxiv.org/abs/2603.21456