Sand-Dust Image Enhancement Algorithm Based on Lab Space

To address the issues of color shift, low sharpness, and low contrast in dust images, this paper proposes an improvement algorithm rooted in Lab space. This method leverages the separation of color and brightness in Lab space for correction and enhancement. Initially, color shift in dust images is corrected in the a and b channels. Next, detail and contrast are improved in the L channel through Laplacian sharpening and contrast-limited adaptive histogram equalization. Adaptive gamma correction is subsequently employed on enhance brightness. Finally, the images are converted back to RGB color space, and histogram stretching is performed, resulting in clear dust images. Experiments show that the proposed method effectively resolves color shift issues and enhances image clarity and detail.