Image super-resolution reconstruction based on deep learning improves the image reconstruction performance by deepening the network.However,its application on resource-limited devices is limited due to the sharp increase in the number of parameters caused by complex networks.To solve this problem,an image super-resolution reconstruction method based on feature aggregation and propagation network is proposed,enriching internal information of images by extracting and fusing features step by step.Firstly,a contextual interaction attention block is proposed to enable the network to learn the rich contextual information of feature maps as well as improve the utilization of features.Then,a multi-dimensional attention enhancement block is designed to improve the network's ability to discriminate the key features and extract high-frequency information in channel dimension and spatial dimension,respectively.Finally,a feature aggregation and propagation block is proposed to effectively aggregate deep detail information,remove redundant information and promote the propagation of effective information in the network.Experimental results on Set5,Set14,BSD100 and Urban100 datasets demonstrate the superiority of the proposed method with clearer details of reconstructed images.
维普
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