Image Dehazing Algorithm Based on Deep Learning

Aiming at the problem that the foggy weather will reduce the image quality, affect the extraction of image information, and reduce the application value of the image, an image defogging algorithm based on deep learning is proposed. Firstly, the original fog image is subjected to single-scale and multi-scale convolution for feature extraction, and then the multi-scale convolution kernel is used to reconstruct the image detail to obtain a rough transmittance propagation map. The atmospheric light value is obtained by using the position and brightness value of the pixel in the original fog image. The guided transmission is used to obtain the fine transmittance propagation map and the previously obtained atmospheric light values to invert the fog-free image. The histogram color correction is finally performed on the fog-free image. The experimental results show that compared with the traditional dehazing algorithm, the algorithm is more natural and has a good visual effect.