Image defogging method and system based on cyclic generative adversarial network

Abstract

The invention discloses an image defogging method and system based on a cyclic generative adversarial network. The method comprises the steps of obtaining a to-be-processed foggy image; inputting the image into a pre-trained dense connection cyclic generative adversarial network, and outputting a fog-free image, wherein the dense connection cyclic generative adversarial network comprises a generator comprising an encoder, a converter and a decoder, the encoder comprises a dense connection layer for extracting features of an input image, the converter comprises an over-conversion layer for combining the features extracted by the encoder stages, the decoder comprises a dense connection layer and a scaled convolutional neural network layer, the dense connection layer is used for restoring the original features of the image, the scaled convolutional neural network layer is used for removing the checkerboard effect of the restored original features, and a finally output fogless image is obtained. The method and system have the advantages that image defogging is carried out based on the cyclic generative adversarial network, the requirement for a pairwise data set is eliminated, the utilization rate of the feature map is improved, the network training efficiency is kept, and the quality of the generated image is improved.

Description

technical field [0001] The invention relates to an image defogging method and system based on a cyclic generative confrontation network, and belongs to the technical field of image processing. Background technique [0002] Under the social conditions of rapid development of informatization, images and videos are the main sources of information for people, and the quality of images also seriously affects the reading and judgment of information. Nowadays, satellite remote sensing systems, aerial photography systems, outdoor monitoring and target recognition systems All the work relies on optical imaging equipment to complete, but due to the appearance of fog and haze, the clarity of the collected photos will be affected, showing the characteristics of reduced contrast, blurred images, and a serious shortage of extractable features. This not only reduces the appreciation of the picture, but also affects the post-processing of the image. Therefore, in order to provide researche...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide an image defogging method and system based on cyclic generative confrontation network, and solve the lack of true pairing faced by the existing image defogging method based on deep learning Data set, lack of feature learning for image defogging based on recurrent generative confrontation network, and generated image artifacts affect the quality of image defogging, etc.

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