  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.
