Multi-scale Iterative Model-guided Unfolding Network for NLOS Reconstruction

Abstract

Non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing diffuse reflection of relay surfaces, and is potentially used in autonomous driving, medical imaging and national defense. Despite the challenges of low signal-to-noise ratio (SNR) and ill-conditioned problem, NLOS imaging has developed rapidly in recent years. While deep neural networks have achieved impressive success in NLOS imaging, most of them lack flexibility when dealing with multiple spatial-temporal resolution and multi-scene images in practical applications. To bridge the gap between learning methods and physical priors, we present a novel end-to-end Multi-scale Iterative Model-guided Unfolding (MIMU), with superior performance and strong flexibility. Furthermore, we overcome the lack of real training data with a general architecture that can be trained in simulation. Unlike existing encoder-decoder architectures and generative adversarial networks, the proposed method allows for only one trained model adaptive for various dimensions, such as various sampling time resolution, various spatial resolution and multiple channels for colorful scenes. Simulation and real-data experiments verify that the proposed method achieves better reconstruction results both in quality and quantity than existing methods.