Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Abstract

Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.