U-NET Image Dehazing Algorithm Combining Wavelet Transform and Attention Mechanism
Images are the basic usage data for many tasks,and have high requirements for their own quality.But the quality of the image is affected by many factors,such as fog in the air.Therefore,the study of image dehazing is very necessary.The emerging deep learning has played an important role in various computer vision tasks,as well as in single image dehazing.Based on the convo-lutional neural network,this paper proposes and designs a U-NET image dehazing model that combines wavelet transform and atten-tion mechanism.Wavelet transform replaces the up and down sampling in the original U-NET,and retains more detailed informa-tion.At the same time,the pixel attention mechanism and the channel attention mechanism are combined into an attention module,which is parallel to the U-NET module and exists as a feature supplement.In the visual effect and quantitative analysis,it is proved that the model has better dehazing effect.
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