A low-light-level image enhancement algorithm combining Retinex and Transformer
XinPu Zhao, Liang Li · 2024
The Low Light Image Enhancement (LLIE) task aims to restore images with poor lighting conditions and visual effects to images with good lighting conditions and visual effects. However, the enhancement results output by existing methods always include various image degradation such as overexposure, artifacts, and color shift. To alleviate these issues, a low light image enhancement method based on Retinex theory and Transformer is proposed. Designed a dual branch network architecture with Transformer blocks. One branch is used to enhance local details of the image, while the other branch is used to adjust the color and brightness of the enhancement results. The dataset used in the experiment was the LOL dataset, which was 23.42 and 0.81 in the PSNR and SSIM indicators, respectively. Compared with the suboptimal algorithm, it improved by 1.06 and 0.016, respectively. The experimental results show that this method has achieved excellent performance in low light image enhancement tasks and has certain application value.