The invention discloses a deep learning-based image defogging algorithm, and the algorithm is used for removing fog interference in foggy images so as to reduce the influence of fog on the image quality. The algorithm comprises the following steps of: 1, obtaining a training sample set and a test sample set; 2, carrying out HSL space variation on foggy images in the sample sets, extracting local low-brightness characteristics of the foggy images, and carrying out scale zooming and normalization processing on all characteristic components; 3, finding out discrimination perspective ratio so as to enable a depth discrimination neural network to realize adversarial training; 4, training the abovementioned characteristic components by utilizing a depth generation adversarial neural network, and learning to establish a mapping network between the foggy images and perspective ratios; and 5, carrying out a defogging test on the test sample set by utilizing the depth generation neural network. According to the defogging algorithm disclosed by the invention, a mapping relationship between the foggy images and the perspective ratios is established through a deep learning algorithm, so that the problem that a previous defogging algorithm is lack of prior information is effectively solved, and a relatively good defogging effect is achieved.