Low-light image enhancement based on deep convolutional neural networks

Abstract

A reference-free low-illumination image enhancement method based on deep convolutional neural networks is proposed to address the problem that low-illumination image enhancement algorithms do not take into account noise suppression while achieving detail enhancement. First, the illumination and reflection components are extracted from the input lowillumination image based on Retinex theory, and optimised separately, and then the optimised illumination and reflection components are multiplied to obtain the enhanced image. loss to update the network parameters. The experimental results show that our algorithm can effectively enhance the contrast and brightness of low-illumination images compared to existing mainstream algorithms, while maintaining the naturalness of the images.