An Image Dehazing Method Based on Generative Adversarial Network

Abstract

An image defogging method based on a generative confrontation network, the method comprising: 1) obtaining sample data; 2) the real foggy image in the sample data is used as input data of a first generator, and the first generator generates a fog-free image once ; The real fog-free image in the sample data is used as the input data of the second generator, and the second generator generates a foggy image; the first discriminator feeds back the error between the primary foggy image and the real foggy image to the second generator The generator and the second discriminator feed back the error between the primary fog-free image and the real fog-free image to the first generator, the second generator and the first generator reduce the error and improve the authenticity of the generated image; the generator Perform repeated confrontation training with the discriminator to obtain the optimal dehazing network model; 3) image dehazing. The present invention adopts a generative confrontation network structure and a loss function, and network training does not require foggy-foggy matching images of the same scene, while ensuring that the color of the image before and after defogging is not distorted.

Description

technical field [0001] The invention relates to an image defogging method, in particular to an image defogging method based on a generative confrontation network, and belongs to the technical field of computer graphics and image processing. Background technique [0002] With the advancement of science and technology, a large number of outdoor digital images are collected and analyzed for various scientific research and production practices, such as target detection, terrain classification, outdoor photography, etc. However, due to the existence of water vapor or suspended particles in the air in the outdoor environment, the images collected outdoors are often accompanied by fog or haze, which causes a series of degradation phenomena such as reduced image contrast, missing parts of the scene, and color shifts. The effective information of the image is a great obstacle. Therefore, it is very important and necessary to find an effective method for digital image defogging, and ...

AI Technical Summary

Problems solved by technology

However, the algorithm still has problems such as color degradation and insufficient transmittance estimation. More research based on DCP aims to solve these problems.

Although the image defogging algorithm develops rapidly, due to the under-constrained characteristics of the image defogging problem itself, the prior information is not sufficient, and various prior assumptions are often accompanied by new problems when solving a certain type of problem. It is very difficult to artificially analyze and find an accurate prior model

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