This paper proposes a dusty image enhancement algorithm based on the RGB color balance method to address the problem of color difference in low-contrast and low-definition outdoor images in dusty environments. The method mainly includes two tasks： color correction and contrast enhancement. First， in view of the particularity of the color distribution of dust images and the illumination mechanism assumed by the gray world algorithm， an RGB color balance method （RGBCbm） that maintains the mean value of the color component is proposed such that the RGB three-channel component is stretched according to the mean value of the color component， which effectively removes the color curtain problem caused by dust in images. The multi-scale retinal enhancement algorithm with color restoration （MSRCR） is further used to improve the color correction results. Subsequently， the relative global histogram stretching （RGHS） method combined with the Lab color model is used to enhance and correct the contrast， color， and brightness of the image. Test results and verification of the proposed algorithm on experimental data show that the algorithm can effectively solve the color difference problem in various dust-degraded images and enhance the clarity of image details while improving the color richness and contrast of the image. In the quantitative comparison with other advanced algorithms， the highest underwater image quality measure （UIQM） and image contrast index （Conl） reach 0.602 and 0.994， respectively， which are 0.140 and 0.018 higher than those of other algorithms.