Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data

TL;DR

This paper introduces curvature regularization models for non-line-of-sight imaging from under-sampled data, improving reconstruction quality and efficiency using GPU-accelerated algorithms based on ADMM.

Contribution

It proposes novel curvature regularization models and efficient GPU-based ADMM algorithms for NLOS imaging, achieving state-of-the-art results in compressed sensing scenarios.

Findings

State-of-the-art reconstruction quality on synthetic and real datasets

Efficient GPU implementation with balanced quality and speed

Effective in compressed sensing and under-sampled data conditions

Abstract

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly possibility to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.