A multi-stage feature distillation-weighted lightweight image super-resolution network
To address the issues of insufficient receptive fields for extracting low-level features and the lack of reinforcement for local key features in lightweight networks,this paper proposed a multi-stage feature distillation-weighted lightweight image super-resolution network LMSWN.Firstly,a pyr-amid-like module is employed to expand the receptive field during shallow feature extraction,integrate feature information of different scales,and enrich the information flow of the network.Secondly,a multi-stage residual distillation-weighted module is designed to enhance the ability of square convolution to extract local key features,recover more detailed information,and improve reconstruction perform-ance.At the same time,the combination of channel separation and 1 × 1 convolution realizes gradual distillation of features,reducing the number of network parameters.Finally,two adaptive parameters are introduced to jointly learn the features of the two branches of the multi-stage residual distillation-weighted module,enhancing the attention to different levels of feature information and further enhan-cing the representation ability of the network.Experimental results show that the proposed network is fully validated on five benchmark datasets:Set 5,Set 14,BSDS 100,Urban 100,and Manga 109,and its performance exceeds the current mainstream lightweight network.
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