Low-light image enhancement has been an actively researched area for decades and has produced excellent night-time single-image and video restoration methods (as explored in Seminar 1). Despite these comprehensive studies, the problem of extreme low-light stereo & Light Field (LF) image restoration has been mostly ignored. Addressing extreme low-light stereo and LF restoration 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× speed-up with 15 − 100× lower floating point operations, suitable for real-world deployment.


To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. For each scene, we captured four LFs, one with near-optimal exposure and ISO settings and the others at different levels of low-light conditions varying from low to extreme low-light settings. We also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. Existing single-frame low-light enhancement techniques do not harness the geometric cues present in different LF views and so lead to either blurry or too noisy restorations. Consequently, we also propose deep neural network architectures for LFs. Our networks not only perform visual enhancement of each LF view but also preserve the epipolar geometry across views. We achieve this by extracting both global and view-specific features and later appropriately fusing them using our RNN-inspired feedforward network. Our LF networks can also be used for low-light enhancement of single-frame images, despite they being engineered for LF data. We do so by proposing a transformation to convert any single-frame DSLR image into a pseudo-LF. This allows the same architecture to be used for both LF & single-image low-light enhancement. With all the above advantages intact, our latest LF based neural network offers considerable speed-up with a significantly lower memory footprint.