Sand-dust weather causes low contrast as well as color distortions in outdoor shots, which has a significant impact on outdoor vision applications, particularly on autonomous cars. This autonomous car system analyses still images (photos) in an offline manner. By processing these images offline, this proposed study aims to improve visibility in dusty conditions. An effective strategy for enhancing sand dust photos is provided in this study to mitigate the color cast and low contrast in the image produced by weather with sand and dust. In this proposed study, color compensation and color correction methods are used to enhance sand-dust images to a higher level. Prior to white balancing, a color compensation method is used to fix the initial color cast, which damaged the dusty image. In this proposed method, color compensation algorithm uses numerous yellow channel information produced by sand dust scattering to compensate for the blue and green channel information. While color compensation removes the sand-dust cast, it also reduces the image detail. After that, using color correction algorithm the obtained image is converted from the RGB to the HSV color space, and the new color divergence is avoided by using the CLAHE to raise the V component. In order to maintain edges and minimize noise, the Laplacian filter were used prior to converting the final image back to RGB. Finally, this proposed method exhibits a 1% higher mean value, a 1.13% higher standard deviation, and a 1% higher entropy compared to the existing methods used.