ConvRML: High-Quality Lensless Imaging with Random Multi-Focal Lenslets

TL;DR

This paper introduces hardware and software innovations, including a random multi-focal lenslet mask and a ConvNeXt-based reconstruction network, significantly enhancing the quality of lensless imaging in compact cameras.

Contribution

It presents a novel combination of hardware design and deep learning architecture to improve lensless imaging quality, supported by a new dataset and standardized evaluation methods.

Findings

Up to 6.68 dB PSNR improvement over previous architectures

RML phase mask reduces multiplexing and improves measurement quality

Established a dataset with 100,000 measurements for training and evaluation

Abstract

Mask-based lensless imagers use simple optics and computational reconstruction to design compact form factor cameras with compressive imaging ability. However, these imagers generally suffer from poor reconstruction quality. Here, we describe several advances in both hardware and software that result in improved lensless imaging quality. First, we use a precision-manufactured random multi-focal lenslet (RML) phase mask to produce improved measurements with reduced multiplexing. Next, we implement a ConvNeXt-based reconstruction architecture, which provides up to 6.68 dB improvement in peak signal-to-noise ratio over state-of-the-art attention-based architectures. Finally, we establish a parallel imaging setup that simultaneously images a scene with RML, diffuser and lens systems, with which we collect datasets with 100,000 measurements for each system, to be used for reconstruction model training and evaluation. Using this standardized system, we quantify the improved measurement quality of the RML compared to a diffuser using the modulation transfer function and mutual information. Our ConvRML system benefits from both the optical and the computational developments presented in this work, and our contributions establish resources to support continued development of high-quality, compact, and compressive lensless imagers.