Mixed residual learning and guided filtering image dehazing algorithm

In order to solve the problem of image clarity and contrast degradation in fog scene image restoration, a single image defogging algorithm based on residual learning and guided filtering was proposed. The residual network was constructed by using foggy images and corresponding clear images. Multi-scale convolution is used to extract more detailed haze features. Taking advantage of the anisotropy of the guided filter, the image after the residual network is filtered to maintain the image edge characteristics, and a clearer fog-free image is obtained. The experimental results show that, compared with DCP algorithm, CAP algorithm, SRCNN algorithm, DehazeNet algorithm and MSCNN algorithm, On synthetic foggy images, the PSNR reaches 27.840 3/dB at the highest, the SSIM value reaches 0.979 6 at the highest, and the running time on natural foggy images reaches 0.4 s at the lowest. and the subjective evaluation and objective evaluation are better than other comparison algorithms. Proposed to fog algorithm not only to the fog effect is better, and faster, with strong practical value.