Image defogging algorithms based on multiscale convolution neural network

Aiming at the problem that the traditional fogging algorithm, which is mostly based on prior knowledge and hypothesis can hardly be satisfied in practice, a kind of end-to-end convolutional neural network is proposed, by learning the mapping relationship between foggy images and clear images, image defogging can be realized directly. First, the algorithm is adopted by multi-scale mapping. Through multiscale convolution, it can extract haze features with more detailed information. Secondly, deconvolution is used to reduce the complexity of computer training network. Finally, Combining the the parallel algorithm of shallow layer and deep layer, the pseudo-pixels in the feature map will be deleted so as to improve the quality of image restoration without fog. The experimental results show that the proposed algorithm is superior to other algorithms in both natural fog images and composite fog images, and the composite fog images have achieved good performance on two important image evaluation indexes of SSIM and PSNR.