  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.
