DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

Abstract

In this paper, we propose an end-to-end low-light image enhancement network based on the YCbCr color space to address the issues encountered by existing algorithms when dealing with brightness distortion and noise in the RGB color space. Traditional methods typically enhance the image first and then denoise, but this amplifies the noise hidden in the dark regions, leading to suboptimal enhancement results. To overcome these problems, we utilize the characteristics of the YCbCr color space to convert the low-light image from RGB to YCbCr and design a dual-branch enhancement network. The network consists of a CNN branch and a U-net branch, which are used to enhance the contrast of luminance and chrominance information, respectively. Additionally, a fusion module is introduced for feature extraction and information measurement. It automatically estimates the importance of corresponding feature maps and employs adaptive information preservation to enhance contrast and eliminate noise. Finally, through testing on multiple publicly available low-light image datasets and comparing with classical algorithms, the experimental results demonstrate that the proposed method generates enhanced images with richer details, more realistic colors, and less noise.