Fast and Efficient Restoration of Extremely Dark Light Fields

Mohit Lamba, Kaushik Mitra; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1361-1370

The ability of Light Field (LF) cameras to capture the 3D geometry of a scene in a single photographic exposure has become central to several applications ranging from passive depth estimation to autonomous driving. But these applications cannot rely on LF captured in low-light conditions due to excessive noise and poor image photometry. The existing low-light enhancement techniques are inappropriate for mitigating this problem as they do not leverage LF's multi-view perspective and give blurry restorations. The recent L3Fnet algorithm alleviates this problem reasonably, but its enormous time and memory complexity make it unaffordable for real-world applications. Thus, we propose a three-stage network that is simultaneously much faster and more accurate. We are more accurate because the three stages compute three complementary features: global, local, and view specific features, which are then fused by our RNN inspired feedforward network to restore LF views. We are faster because we restore multiple views simultaneously and so require less number of forward passes. Besides these advantages, our network is flexible enough to restore a m xm LF during inference even if trained for a smaller n xn (n<m) LF without any finetuning. Extensive experiments on real low-light LF demonstrate that compared to state-of-the-art, our model can achieve up to 1 dB higher restoration PSNR, with 9 xspeedup, 23% smaller model size and about 5 xlower floating-point operations.