Unrolled primal-dual networks for lensless cameras.
Opt Express
; 30(26): 46324-46335, 2022 Dec 19.
Article
em En
| MEDLINE
| ID: mdl-36558589
ABSTRACT
Conventional models for lensless imaging assume that each measurement results from convolving a given scene with a single experimentally measured point-spread function. These models fail to simulate lensless cameras truthfully, as these models do not account for optical aberrations or scenes with depth variations. Our work shows that learning a supervised primal-dual reconstruction method results in image quality matching state of the art in the literature without demanding a large network capacity. We show that embedding learnable forward and adjoint models improves the reconstruction quality of lensless images (+5dB PSNR) compared to works that assume a fixed point-spread function.
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01-internacional
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MEDLINE
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En
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Opt Express
Ano de publicação:
2022
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Article