2024/03/01 by Yohann Perron, Perron, Yohann, Eric Bezzam +3
Engineering · Physics and Astronomy · #Image Processing Techniques and Applications #Digital Holography and Microscopy #Photoacoustic and Ultrasonic Imaging
paper · pdf · doi:10.48550/arxiv.2403.00537
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.