Non-line-of-sight imaging based on an untrained deep decoder network

In recent years, low-cost high-quality non-line-of-sight (NLOS) imaging by a passive light source has been a significant research dimension. Here, we report a new, to the best of our knowledge, reconstruction method for the well-known "occluder-aided" NLOS imaging configuration based on an untrained deep decoder network. Using the interaction between the neural network and the physical forward model, the network weights can be automatically updated without the need for training data. Completion of the optimization process facilitates high-quality reconstructions of hidden scenes from photographs of a blank wall under high ambient light conditions. Simulations and experiments show the superior performance of the proposed method in terms of the details and the robustness of the reconstructed images. Our method will further promote the practical application of NLOS imaging in real scenes.