Single Image Dehazing Algorithm Based on Mixed Attention
Single image dehazing refers to the process of restoring a hazy image to a clear one through image processing.Recently,as the rapid development of computer image processing,deep learning-based fog removal algorithms have made great progress,but there are still some problems,such as color distortion,incomplete fog removal and so on.To solve these problems,a hybrid attention mecha-nism was designed,combining Transformer attention,channel attention,and pixel attention,and introducing deformable convolution for feature extraction,thereby constructing a single image dehazing network.In order to obtain a better defogging model,the model is trained and debuggable in the data set RESIDE,and good experimental results are obtained:the PSNR metric is improved by 3.5％compared to FFA-Net and by 4.2％compared to GCANet,and SSIM metric is improved by 0.21％compared to FFA-Net and by 1.1％compared to GCANet.
Computer image processingDeep learningTransformer attention mechanismDeformable convolutionRESIDE data set
万方数据
维普
