Adaptive image enhancement method for correcting low-illumination images

In this study, to improve the adaptability of image enhancement in images with low illumination, a colored image correction method based on nonlinear functional transformation according to the illumination-reflection model and multiscale theory is proposed. First, the original RGB image is converted to HSV color space, and the V component is used to extract the illumination component of the scene using the multiscale Gaussian function. Then, a correction function is constructed based on the Weber-Fechner law, and two images are obtained through adaptive adjustments to the image enhancement function parameters based on the distribution profiles of the illumination components. Finally, an image fusion strategy is formulated and used to extract the details from the two images. Compared with the classic algorithm, the proposed algorithm can improve the overall brightness and contrast of an image while reducing the impact of uneven illumination. The enhanced images appear clear, bright, and natural.