Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Rui-Qiang Ma,Shan-Jun Zhang,Xing-Run Shen,Javier Martinez Torres

doi:10.1155/2020/7938060

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.

Highlights

Fog is a natural phenomenon that blurs the scene, reduces visibility, and changes color. is is an annoying problem for photographers because it can reduce image quality
The atmospheric scattering model is based on prior knowledge: one is the fogfree images that have higher contrast than images under severe weather conditions, and the other is light that is often smooth between the scene and the observer. en, local pairing of scene images is improved by using the Markov random fields (MRF) model
In the dark channel prior method, when the image is similar to airlight over a large local region and no shadow is cast on the image of object, conditional generative adversarial network (GAN) is used to carry out for confrontation training, and the problem is solved

Summary

Introduction

Fog is a natural phenomenon that blurs the scene, reduces visibility, and changes color. is is an annoying problem for photographers because it can reduce image quality. Fog is a natural phenomenon that blurs the scene, reduces visibility, and changes color. Considering the particularity of fog image, image processing is based on prominent details, contrast enhancement, and brightness enhancement, which has a certain image enhancement effect in vision, and it is not the result of defogging in essence. In terms of image restoration methods based on physical models, some researchers use the atmospheric scattering model [3] proposed by Mcca’s group to model hazy scenes to solve the problem of hazy degraded images. E result of this processing method makes the contrast of the image greatly improved, but there is a large halo phenomenon, and the scene appears with a Mathematical Problems in Engineering certain degree of color deviation, which leads to the unreal image Ratio restored the fog-free image of the scene. e result of this processing method makes the contrast of the image greatly improved, but there is a large halo phenomenon, and the scene appears with a Mathematical Problems in Engineering certain degree of color deviation, which leads to the unreal image

Methods

Results

Conclusion