Isolating Signals in Passive Non-Line-of-Sight Imaging using Spectral Content

In real-life passive non-line-of-sight (NLOS) imaging there is an overwhelming amount of undesired scattered radiance, called clutter, that impedes reconstruction of the desired NLOS scene. This paper explores using the spectral domain of the scattered light field to separate the desired scattered radiance from the clutter. We propose two techniques: The first separates the multispectral scattered radiance into a collection of objects each with their own uniform color. The objects which correspond to clutter can then be identified and removed based on how well they can be reconstructed using NLOS imaging algorithms. This technique requires very few priors and uses off-the-shelf algorithms. For the second technique, we derive and solve a convex optimization problem assuming we know the desired signal's spectral content. This method is quicker and can be performed with fewer spectral measurements. We demonstrate both techniques using realistic scenarios. In the presence of clutter that is 50 times stronger than the desired signal, the proposed reconstruction of the NLOS scene is 23 times more accurate than typical reconstructions and 5 times more accurate than using the leading clutter rejection method.