Cycle-enhance: low-light image enhancement based on CycleGan

Abstract

Distortion-free enhancement on images captured under low-light conditions has always been a challenging problem in computer vision. To address these problems, this paper proposes an end-to-end network, which can learn the map way of low-light images to normal-light images from unpaired low-light and normal-light datasets. The network is consisted of dual branches, the upper branch is a refinement branch focusing on noise suppression, and the lower branch is a global reconstruction branch based on light-weight Transformer. The discrimination network adopts the multi-scale discrimination structure of feature pyramid to enhance the global consistency and avoid local overexposure. Qualitative and quantitative experimental results show that the proposed method can effectively suppress the generation of artifacts and noise amplification of enhanced images.