Image denoising algorithm based on self-attention residual network

Image denoising algorithm based on depth learning generally uses convolution sparse self-coding network as the main framework of the denoising network. However, although convolution sparse self-coding network can effectively suppress the noise information in the image, it has the problem of loss of certain details in the image after denoising. Aiming at this defect, on the basis of convolutional sparse self-encoding network, the detail information of each layer feature map is extracted from the output of each encoder layer using self-attention mechanism, and the detail information is integrated into the input layer of the corresponding decoder using residual connection method. Experimental results show that compared with the traditional convolutional self-coding noise reduction network, the proposed convolutional self-coding network based on self-attention residuals can effectively improve the level of network noise reduction. At the same time, compared with the mainstream noise reduction network, the proposed algorithm can also achieve better noise reduction effect.