A low-light-level image enhancement algorithm combining Retinex and Transformer

Xinpu Zhao; Liang Li

doi:10.1117/12.3029685

21 June 2024 A low-light-level image enhancement algorithm combining Retinex and Transformer

Xinpu Zhao, Liang Li

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672O (2024) https://doi.org/10.1117/12.3029685
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinpu Zhao and Liang Li "A low-light-level image enhancement algorithm combining Retinex and Transformer", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672O (21 June 2024); https://doi.org/10.1117/12.3029685

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KEYWORDS

Image enhancement

Transformers

Visualization

Light sources and illumination

Image restoration

Education and training

Feature fusion

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