Learning to See Moving Objects in the Dark

Abstract

Video surveillance systems have wide range of utilities, yet easily suffer from great quality degeneration under dim light circumstances. Industrial solutions mainly use extra near-infrared illuminations, even though it doesn't preserve color and texture information. A variety of researches enhanced low-light videos shot by visible light cameras, while they either relied on task specific preconditions or trained with synthetic datasets. We propose a novel optical system to capture bright and dark videos of the exact same scenes, generating training and groud truth pairs for authentic low-light video dataset. A fully convolutional network with 3D and 2D miscellaneous operations is utilized to learn an enhancement mapping with proper spatial-temporal transformation from raw camera sensor data to bright RGB videos. Experiments show promising results by our method, and it outperforms state-of-the-art low-light image/video enhancement algorithms.