Image denoising method based on edge feature fusion network

Most of the current image denoising algorit hms usually cause the loss of edge detail information of the image while removing noise. Aiming at this problem, an image denoising method based on edge feature fusion is proposed. First,the edge information of the image is extracted by the edge extraction network based on Canny operator. Because the Canny operator does not need to be trained, the denoising time is shortened to a large extent. Secondly, the initial denoising network based on dense residual connections is used to ensure the stability of the training and avoid the disappearance of the gradient to achieve the initial denoising of the image. Finally, through the fusion network based on channel and spatial attention mechanism, the extracted edge information image is fully fused with the preliminary denoised image, and the relatively important edge information is adaptively allocated with more weight, and the edge details of the image are enhanced, so as to get a clear image with more edge information. Experimental results show that on BSD68 and Set12 datasets, compared with the common denoising methods such as DnCNN and BM3D, the average PSNR of the proposed denoising method is 0.13 dB and 0.29 dB higher than DnCNN, and 0.76 dB and 0.82 dB higher than BM3D, respectively. In terms of visual effects, more image details are retained, and the denoising rate is also greatly improved.