Single Image Dehazing Algorithm Based on Cycle Generative Adversarial Networks and Transformer
Aiming at the problem of overfitting in traditional dehazing algorithms trained on paired data-sets,a non-paired image dehazing network model based on density and depth decomposition was improved with a self-enhancing scaling network.Introducing the Transformer mechanism and deeply integrating it with deep convolutional neural networks for network module deep fusion,a CT-Nets image dehazing algo-rithm based on cycle generative adversarial networks and Transformers trained on unpaired datasets was proposed.The depth information and scattering coefficient eigenvalues of the input image were extracted,and the atmospheric scattering model was used to restore the real fog concentration information in different scenes as much as possible to improve the subjective visual quality of the defogged image.Based on Swin-Transformer,a self-enhancing refinement layer to obtain finer-grained information was designed to im-prove the generalization ability of the model and the authenticity of the final predicted image.The experi-mental results show that compared to the dehazing via decomposing transmission map into density and depth network model,the peak signal-to-noise ratio and structural similarity of the CT-Nets image dehaz-ing algorithm are improved by 4%and 4.1%,respectively.
deep learningsingle image dehazingself-supervised networkcycle generative adversarial
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