Non-line-of-sight imaging aims at recovering obscured objects from multiple scattered lights. It has recently received widespread attention due to its potential applications, such as autonomous driving, rescue operations, and remote sensing. However, in cases with high measurement noise, obtaining high-quality reconstructions remains a challenging task. In this work, we establish a unified regularization framework, which can be tailored for different scenarios, including indoor and outdoor scenes with substantial background noise under both confocal and non-confocal settings. The proposed regularization framework incorporates sparseness and non-local self-similarity of the hidden objects as well as the smoothness of the signals. We show that the estimated signals, albedo, and surface normal of the hidden objects can be reconstructed robustly even with high measurement noise under the proposed framework. Reconstruction results on synthetic and experimental data show that our approach recovers the hidden objects faithfully and outperforms state-of-the-art reconstruction algorithms in terms of both quantitative criteria and visual quality.

Non-line-of-sight (NLOS) imaging enables people to see a hidden scene based on multiple interaction information between the object and the carrier. There have been numerous studies focusing on the physical modeling of photon scattering, but few have explored the detection process, which also plays a vital role. In this paper, we put forward a novel, to the best of our knowledge, detection methodology for NLOS imaging based on time-sequential first photon (TSFP) data. We verify the method with both synthetic and experimental data, showing a dramatic reduction in acquisition time cost compared with traditional methods for the same reconstruction quality. This work may contribute to real-time and photon-starved NLOS imaging for practical applications.

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