Enhancing Low-Light Images: A Novel Approach Combining Anisotropic Diffusion and Retinex

Low-light image enhancement is a significant and challenging task in the field of computer vision. This paper presents a method combining enhanced anisotropic diffusion techniques with Retinex theory for low-light image enhancement. Initially, the algorithm employs the HSV color space to decompose the image, with a focus on processing the luminance component. In the crucial illumination estimation step, an improved anisotropic diffusion technique is proposed. This technique adaptively modulates the diffusion process based on the image’s gradient information, effectively capturing both local and global illumination details and ensuring precise and smooth illumination estimation. Subsequently, the paper proposes a global illumination balancing strategy to maintain the natural light and dark relationships within the image. Finally, an adaptive illumination enhancement function is developed, significantly improving the visibility in dark areas while preserving the essential details of the image. Experimental validation demonstrates that this method outperforms existing approaches in low-light image enhancement, paving new pathways for applications in low-light vision.