基于对比学习的低光照图像增强

Contrastive Learning for Low-light Image Enhancement

针对低照度条件下获取的图像能见度低和质量差的问题,提出了一种基于对比学习的低光照图像增强方法。文中将图像转换任务的方法运用于低光照图像增强,其挑战在于低光域和正常光域之间的差异对于像素级恢复来说过于巨大和复杂,因此所提方法将其分为两步。首先使用基于Retinex理论的传统算法对原始低光图像进行初步地光照增强,以缩小两个域之间的差异,获取低光域和正常光域之间的中间状态。然后基于对比学习将后续的增强任务分解成两个阶段,即内容增强和降质学习,以此实现两个域之间的映射。对比学习可以进一步加强网络的表征能力,最终达到高自然度的图像恢复。大量实验证明了所提方法的高效性,其能够有效地增强低光照图像,图片质量和细节保留能力优于多种先进的光照增强方法。

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.