Abstract: To address the issues of color bias, low contrast, and poor visibility in dust-affected images, a two-stage strategy for dust image enhancement algorithm is proposed. The algorithm comprises a Dust Image color correction algorithm and a residual fusion-based haze removal network. In the first stage, a weighted gray-world theory based on image saturation in the Lab color space is proposed for color correction, effectively addressing the color bias issue in dust images. In the second stage, a residual fusion-based dust and haze removal network is designed to enhance the contrast and clarity of the images. The experimental results show that the algorithm can effectively remove color bias and enhance the visibility of image details while improving image contrast. Compared to the best results from the comparative experiments, the proposed algorithm improves PSNR and SSIM by 2.3380% and 3.0662%, respectively.