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We report deep learning-based design of diffractive all-optical processors for performing arbitrary linear transformations of optical intensity under spatially incoherent illumination. We show that a diffractive optical processor can approximate an arbitrary linear intensity transformation under spatially incoherent illumination with a negligible error if it has a sufficient number of optimizable phase-only diffractive features distributed over its diffractive surfaces. Our analysis and design framework could open up new avenues in designing incoherent imaging systems with an arbitrary set of spatially-varying point-spread functions (PSFs). Moreover, this framework can also be extended to design task-specific all-optical visual information processors under natural illumination.
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Md Sadman Sakib Rahman, Xilin Yang, Jingxi Li, Bijie Bai, Aydogan Ozcan, "Universal linear processing of spatially incoherent light with diffractive optical processors," Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030K (13 March 2024); https://doi.org/10.1117/12.3000301