Coded Illumination for Improved Lensless Imaging

TL;DR

This paper introduces a coded illumination technique for lensless cameras that significantly enhances image reconstruction quality by using optimized illumination patterns and a fast recovery algorithm, demonstrated through simulations and hardware tests.

Contribution

The paper presents a novel coded illumination approach and a low-complexity recovery algorithm to improve lensless imaging quality, addressing measurement system ill-conditioning.

Findings

Coded illumination patterns improve image quality.

Shifting dots and orthogonal patterns perform best.

Proposed algorithm enhances reconstruction accuracy.

Abstract

Mask-based lensless cameras can be flat, thin, and light-weight, which makes them suitable for novel designs of computational imaging systems with large surface areas and arbitrary shapes. Despite recent progress in lensless cameras, the quality of images recovered from the lensless cameras is often poor due to the ill-conditioning of the underlying measurement system. In this paper, we propose to use coded illumination to improve the quality of images reconstructed with lensless cameras. In our imaging model, the scene/object is illuminated by multiple coded illumination patterns as the lensless camera records sensor measurements. We designed and tested a number of illumination patterns and observed that shifting dots (and related orthogonal) patterns provide the best overall performance. We propose a fast and low-complexity recovery algorithm that exploits the separability and block-diagonal structure in our system. We present simulation results and hardware experiment results to demonstrate that our proposed method can significantly improve the reconstruction quality.