The work aims to design a new prior image defogging algorithm based on contrast and saturation tosolve the low clarity and resolution in current image printing, and improve the quality and perception of images, so that the fog image details can be clearer and more efficient in processing. The initial estimation of the transmission map was first performed on the captured fog image with contrast, saturation and luminance as prior. Then the contrast and saturation of the image were enhanced with the help of a gamma-corrected nonlinear inverse strategy to prevent the transmission estimation from being too high or too low, the coarse transmission estimation of the nonlinear inverse strategy was applied for optimization by guided filtering. And finally, the recovered image was further enhanced with luminance using a nonlinear contrast stretching method. Through experimental tests, it is concluded that for a 450×450 image, the algorithm proposed in this paper improved the PNSR and SSIM values by 9.220 4 and 0.332 7, respectively, and reduced the processing time by 0.079 38 s compared with the current state-of the-art algorithm. In conclusion, the image restoration effect is better. It has more detail, and higher clarity and efficiency.