Kingshott, O;
Antipa, N;
Bostan, E;
Akşit, K;
(2022)
Unrolled primal-dual networks for lensless cameras.
Optics Express
, 30
(26)
pp. 46324-46335.
10.1364/OE.475521.
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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.
| Type: | Article |
|---|---|
| Title: | Unrolled primal-dual networks for lensless cameras |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/OE.475521 |
| Publisher version: | https://doi.org/10.1364/OE.475521 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10162239 |
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