Low-light Image Enhancement Algorithm Based on Improved Multi-scale Retinex with Adaptive Brightness Compensation
Xinxin Wang, Yi Hong Ru, Mingyang Sun, Zhaozheng Zhang · 2024
Low-light image enhancement is a crucial low-level visual task, essential for improving image quality, enhancing visual effects, and facilitating higher-level image analysis and processing. This paper introduces an improved multi-scale Retinex algorithm for enhancing low-light images. Initially, the low-light image is converted to the HSV color space, and the improved multi-scale Retinex is applied to the V (brightness) channel to obtain the illumination component and the optimized reflection component, the latter serving as the enhanced brightness channel. Subsequently, a brightness compensation strategy based on the estimated illumination component is proposed for adaptive adjustment of the enhanced brightness channel. Finally, by restoring the brightness values in high-luminance areas, the enhanced brightness channel V′ is formed, combined with the H and S channels, and converted back to the RGB color space to produce the final enhanced image. Experimental results show that this method is effective for low-light images with uniform or non-uniform lighting, significantly improving image quality and providing an excellent visual experience. The code is hosted in the GitHub repository at https://github.com/xinxin6809/IMR-ABC.