Dust Weather Image Clarity Algorithm Based on Color Adjustment and Dark Channel Dehazing

To solve the problem of color deviation and low contrast in dust weather images, a image clarity algorithm based on color adjustment and dark channel fogging is proposed from the perspective of image restoration. The improved Gaussian model is first used in the proposed algorithm to correct and stretch the RGB three channels, solving the problem of image color deviation. On this basis, the pixel level atmospheric light value map is calculated based on the light and dark channel data, and the transmittance of the sky and prospective areas is modified by a threshold, which is obtained based on the statistical characteristics of those areas, sloving the distortion problem of the sky and prospective areas in the restored image. Finally, the overall brightness of the restored image is optimized by iteratively adjusting the atmospheric light value. The simulation results show that the proposed algorithm can effectively solve the distortion problem of the sky and prospective areas, ensure the appropriate brightness, enhance the image contrast, and restore the real color of the image scene.