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.

非视线成像旨在从多个散射光中恢复被遮挡的物体。由于其潜在的应用，例如自动驾驶、救援行动和遥感，它最近受到了广泛的关注。然而，在测量噪声高的情况下，获得高质量的重建仍然是一项具有挑战性的任务。在这项工作中，我们建立了一个统一的正则化框架，可以针对不同的场景进行定制，包括在共焦和非共焦设置下具有大量背景噪声的室内和室外场景。所提出的正则化框架结合了隐藏对象的稀疏性和非局部自相似性以及信号的平滑性。我们表明估计的信号，反照率，即使在所提出的框架下，即使存在高测量噪声，也可以稳健地重建隐藏对象的表面法线。合成和实验数据的重建结果表明，我们的方法忠实地恢复了隐藏对象，并且在定量标准和视觉质量方面都优于最先进的重建算法。