Year: 2024
Type: conference-paper
Abstract: In this paper, we present a modular approach for reconstructing lensless measurements. It consists of three components: a newly-proposed pre-processor, a physics-based camera inverter to undo the multiplexing of lensless imaging, and a post-processor. The pre-and post-processors address noise and artifacts unique to lensless imaging before and after camera inversion respectively. By training the three components end-to-end, we obtain a 1.9 dB increase in PSNR and a $14 \%$ relative improvement in a perceptual image metric (LPIPS) with respect to previously proposed physics-based methods. We also demonstrate how the proposed pre-processor provides more robustness to input noise, and how an auxiliary loss can improve interpretability.
Authors: Yohann Perron, Eric Bezzam, Martin Vetterli
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4402916944