Non-line-of-sight reconstruction via structure sparsity regularization

TL;DR

This paper introduces a structure sparsity regularization method for non-line-of-sight imaging that improves reconstruction quality in low SNR conditions by effectively denoising and reconstructing occluded objects.

Contribution

It proposes a novel SS regularization technique integrated with nuclear norm penalization within the DLCT model for enhanced NLOS image reconstruction.

Findings

Outperforms state-of-the-art algorithms in synthetic and real datasets.

Achieves high-quality reconstructions with short exposure and low SNR.

Robustly reconstructs occluded objects in challenging scenarios.

Abstract

Non-line-of-sight (NLOS) imaging allows for the imaging of objects around a corner, which enables potential applications in various fields such as autonomous driving, robotic vision, medical imaging, security monitoring, etc. However, the quality of reconstruction is challenged by low signal-noise-ratio (SNR) measurements. In this study, we present a regularization method, referred to as structure sparsity (SS) regularization, for denoising in NLOS reconstruction. By exploiting the prior knowledge of structure sparseness, we incorporate nuclear norm penalization into the cost function of directional light-cone transform (DLCT) model for NLOS imaging system. This incorporation effectively integrates the neighborhood information associated with the directional albedo, thereby facilitating the denoising process. Subsequently, the reconstruction is achieved by optimizing a directional albedo model with SS regularization using fast iterative shrinkage-thresholding algorithm. Notably, the robust reconstruction of occluded objects is observed. Through comprehensive evaluations conducted on both synthetic and experimental datasets, we demonstrate that the proposed approach yields high-quality reconstructions, surpassing the state-of-the-art reconstruction algorithms, especially in scenarios involving short exposure and low SNR measurements.