Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction

TL;DR

This paper introduces a modular learned reconstruction method for lensless imaging that enhances robustness and generalization across different mask types, reducing calibration effort and enabling transfer learning.

Contribution

It proposes a pre-processor prior within a modular framework, demonstrating its necessity and effectiveness for various lensless imaging approaches and datasets.

Findings

Pre-processor improves image recovery quality.

Method generalizes across different mask types.

Open-source datasets and software provided.

Abstract

Lensless cameras disregard the conventional design that imaging should mimic the human eye. This is done by replacing the lens with a thin mask, and moving image formation to the digital post-processing. State-of-the-art lensless imaging techniques use learned approaches that combine physical modeling and neural networks. However, these approaches make simplifying modeling assumptions for ease of calibration and computation. Moreover, the generalizability of learned approaches to lensless measurements of new masks has not been studied. To this end, we utilize a modular learned reconstruction in which a key component is a pre-processor prior to image recovery. We theoretically demonstrate the pre-processor's necessity for standard image recovery techniques (Wiener filtering and iterative algorithms), and through extensive experiments show its effectiveness for multiple lensless imaging approaches and across datasets of different mask types (amplitude and phase). We also perform the first generalization benchmark across mask types to evaluate how well reconstructions trained with one system generalize to others. Our modular reconstruction enables us to use pre-trained components and transfer learning on new systems to cut down weeks of tedious measurements and training. As part of our work, we open-source four datasets, and software for measuring datasets and for training our modular reconstruction.