An image super-resolution reconstruction method based on residual dense attention networks is pro-posed to address the problems of detail loss and blurred image edges in existing image super-resolution recon-struction algorithms.The method employs a structure of dense connections and residual connections to construct the residual network,making full use of the information interaction between low-level features and high-level features to extract higher-level image features.Meanwhile,fused channel attention and spatial attention adaptive-ly select important features and weighted fusion of these features,thus better recovering the texture details of the image.Experimental results show that our proposed method performs well in terms of peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).
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