Image Restoration via Deep-Structured Stochastically Fully-Connected Conditional Random Fields (DSFCRFs) for Very Low-Light Conditions
Audrey G. Chung, Mohammad Javad Shafiee, Alexander K.C. Wong · 2016
Very low-light conditions are problematic for current robotic vision algorithms as captured images are subject to high levels of ISO noise. We propose a novel deep-structured stochastically fully-connected conditional random field (DSFCRF) model for image restoration in very low-light conditions. The DSFCRF model combines the improved performance of deep-structured graphical models with the reduced complexity of stochastically fully-connected random fields. The proposed model was compared to state-of-the-art image restoration methods using a set of images contaminated with synthetically generated noise and a set of natural images captured in very low-light conditions. Experimental results indicate the potential of DSFCRFs for low-light image restoration.