Real-Time Restoration of Dark Stereo Images

Mohit Lamba, M. V. A. Suhas Kumar, Kaushik Mitra; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4914-4924

Low-light image enhancement has been an actively researched area for decades and has produced excellent night-time single-image, video, and Light Field restoration methods. Despite these comprehensive studies, the problem of extreme low-light stereo image enhancement has been mostly ignored. Addressing this problem can enable night-time capabilities to several applications such as smartphones and self-driving cars. We propose a light-weight and fast hybrid U-net architecture for low-light stereo image enhancement. In the initial few scale spaces, we process the left and right features individually, because the two features do not align well due to large disparity. At coarser scale-spaces, the disparity between left and right features decreases and the network's receptive field increases. We use this fact to reduce computations by simultaneously processing the left and right features, which also benefits epipole preservation. As our architecture does not use any 3D convolution for fast inference, we use an Epipole-Aware loss module to train our network. This module computes quick and coarse depth estimates to better enforce the epipolar constraints. Extensive benchmarking in terms of visual enhancement and downstream depth estimation shows that our architecture not only performs significantly better but also offers 4-60 xspeed-up with 15-100 xlower floating point operations, suitable for real-world deployment.