Retinex inspired Unrolling with Cooperative Prior Architecture Search for Low light Image Enhancement
Low light image enhancement plays very important roles in low level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex inspired Unrolling with Architecture Search (RUAS) to construct lightweight yet effective enhancement network for low light images in real world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low light images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference free learning strategy to discover low light prior architectures from a compact search space, RUAS is able to obtain a top performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state of the art methods.
CVPR