Abstract: Insufficient lighting in image capturing can significantly degrade the visibility and quality of images.To tackle this problem,this paper proposes a low-light image enhancement network based on contrastive learning.It is a challenging work to apply image-to-image translation task to image enhancement since the gap between low and normal light is too huge and complex for pixel-level restoration.Therefore,the proposed method takes in two steps.In order to build intermediate states that lie between the low and normal light,this paper first adopts a traditional method based on Retinex theory to initially enhance the low-light images.Second,in order to make the mappings between two domains,the subsequent enhancement is decomposed into two stages,content enhancement and degradation learning.This work is based on contrastive learning,which can enhance the representation ability of the networks,and achieves high-naturalness recovery.Extensive experimental results demonstrate the efficiency of proposed method,which can enhance the low-light image effectively with better image quality and detail restoration ability than the SOTA low-light image enhancement methods.

Key words: Insufficient lighting, Image enhancement, Image-to-Image translation, Contrastive learning, Degradation learning