Efficient Image Super-Resolution Reconstruction Based on Convolutional Neural Networks and Transformer
Deep learning has significantly advanced image super-resolution reconstruction techniques.However,the computational and memory costs associated with complex operations have limited their practical applications.To address this issue,a novel algorithm is proposed,which integrates Transformer and Convolutional Neural Networks(CNNs)while incorporating dilated convolutions and depthwise separable convolutions.Experimental results on five benchmark datasets demonstrate that the proposed EHN model efficiently extracts super-resolution features,achieving comparable or even better super-resolution performance with fewer parameters and inference time compared to existing methods.Specifically,under×2,×3,and×4 magnification factors,the inference time of EHN is merely 18.4%,18.9%,and 20.3%of that of existing networks,respectively.This advantage is crucial for scenarios involving the processing of large volumes of images,significantly reducing computational time and resource consumption,thereby enhancing overall efficiency.
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