Since the dark channel prior algorithm has dull restoration effect and too long processing time, a fast dehazing algorithm for single image based on relative transmittance estimation was proposed. On the basis of the analysis of relationship between field depth under haze condition and minimum image of color channel (RGB) images, a preliminary transmittance was estimated through the relative amount of field depth, and then it was adjusted with an improved mean filter. At last, the clear image could be recovered by the atmospheric scattering model and the brightness was enhanced to improve its visual effects. The estimation of transmittance in this paper is simple and effective, the restored images are clear and natural, and have high detail visibility and scenery layering. The experimental results show that the proposed algorithm has great improvement in image dehazing quality and computational time, which is propitious to achieve real-time application.

Concerning that the parameter estimation in defogging algorithms based on image restoration is easy to cause the loss of scene information, a new defogging algorithm for single image was proposed. On the basis of the dark channel prior method, the atmospheric scattering model was analyzed and then the influence to dark channel image caused by fog distribution was summarized, which is the basis for adding fog to the outdoor images. The transmittance was estimated through the field depth relationship between the fog added reference image and the outdoor image to defogging. The algorithm used physical model and multiple images to complete the estimation of relevant parameters and had a better result in retaining scene information. The experimental results show that the proposed algorithm is more effective than the comparison algorithms, and its processing speed is also improved significantly.