中圖分類號(hào)：TP391文獻(xiàn)標(biāo)識(shí)碼：ADOI:10.19358/j.issn.2097-1788.2024.03.005
引用格式：林承浩，吳麗君.基于CNN-Transformer混合構(gòu)架的輕量圖像超分辨率方法［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2024，43（3）：27-33.

A lightweight image super resolution method based on a hybrid CNN-Transformer architecture

Abstract： In order to address the problem that image super segmentation models based on hybrid architectures usually require high computational cost, this study proposes a lightweight image super segmentation network STSR (Swin Transformer based Single Image Super Resolution) based on a hybrid CNN-Transformer architecture. Firstly, this paper proposes a Feature Enhancement Block (FEB) for parallel feature extraction, which consists of a Convolutional Neural Network (CNN) and a lightweight Transformer Network to extract features from the input image in parallel, and then the extracted features are fused to the features. Secondly, this paper designs a Dynamic Adjustment (DA) module, which enables the network to dynamically adjust the output of the network according to the input image, reducing the network's dependence on irrelevant information. Finally, some benchmark datasets are used to test the performance of the network, and the experimental results show that STSR still maintains a better reconstruction effect under the premise of reducing the number of model parameters.

Key words : image superresolution; lightweighting; Convolutional Neural Network; Transformer