Generative Adversarial Mapping Nets with Multi-layer Perception for Image Dehazing

Haze has an impact on the quality of the image. Single image dehazing is a challenging ill-posed problem. The traditional dehazing methods have some problems, such as color distortion and limited application scope. To overcome these problems, we propose a generative adversarial mapping nets（GAMN） algorithm for image dehazing. In the training, an adversarial learning mechanism between the generative networks and the discriminative networks was used to obtain the optimal solution of parameters. In the testing, the trained generative networks can translate the haze related features to the medium transmission by multilayer Perception, the medium transmission is related to the depth and help to complete dehazing. Experimental results show that the proposed algorithm is closer to the real color compared with the state-of-the-art method. It can restrain noise and dehaze clearly.