Super-resolution Reconstruction of Images Based on Blueprint Separable Residual Distillation Network

The performance of single image super-resolution reconstruction based on standard convolution is limited by the redundancy of the stacked network layers, making it difficult to implement the algorithm on the ground. Moreover, the single residual structure of the feature extraction layer cannot efficiently utilize the feature information obtained from convolution. To address these, this paper proposes a residual distillation reuse module based on the existing residual distillation-based structure to reduce the high-frequency information of the image lost in the residual distillation process. In addition, the base residual block is replaced by a blueprint separable convolution to decouple the spatial correlation of the feature map, such that the weight of highly correlated features can be reduced. As a result, the efficiency of convolution can be improved and the number of parameters can be reduced. We conduct comparative experiments on standard datasets such as Set5 to evaluate the proposed algorithm. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm can be improved by approximately 0.06~0.25 dB and 0.004~0.012, respectively, over the lightweightresidual distillation image super-resolution networks.