An Improved Dark Channel Prior Defogging Algorithm Based on Transmissivity Image Segmentation

In view of the dark channel prior algorithm in dealing with the haze image color distortion in the sky region, atmospheric light value error extraction and scene edge halo effect, a dark channel prior defogging removal method based on transmittance image is proposed in this paper. The input haze image is converted into transmittance image. with guided filtering, the improved MSR algorithm can be used to segment the image into sky region and non-sky region. Minimum filtering and sky transmittance estimation are performed for sky region and non-sky region respectively. The two parts of images obtained by processing are combined, and the transmission is refined by fast guided filtering, and the haze image is defogging removed by combining the atmospheric light value extracted from the sky region to obtain a clear restored image. The experimental results show that the improved minimum filtering algorithm and transmittance estimation method can effectively remove the halo effect at the edge of the depth of field and the color distortion in the sky area, so that the restored image retains more details and has a clearer and natural vision. Compared with the traditional dark channel prior algorithm, the information entropy of the proposed algorithm increases by 12.1% on average, PNSR increases by 6.024% on average, SSIM increases by 15.8%, and MSE decreases by 4.7% on average.