摘要： 針對基于混合構(gòu)架的圖像超分模型通常需要較高計算成本的問題，提出了一種基于CNN-Transformer混合構(gòu)架的輕量圖像超分網(wǎng)絡(luò)STSR（Swin Transformer based Single Image Super Resolution）。首先，提出了一種并行特征提取的特征增強(qiáng)模塊（Feature Enhancement Block，F(xiàn)EB），由卷積神經(jīng)網(wǎng)絡(luò)（Convolutional Neural Network，CNN）和輕量型Transformer網(wǎng)絡(luò)并行地對輸入圖像進(jìn)行特征提取，再將提取到的特征進(jìn)行特征融合。其次，設(shè)計了一種動態(tài)調(diào)整模塊（Dynamic Adjustment，DA），使得網(wǎng)絡(luò)能根據(jù)輸入圖像來動態(tài)調(diào)整網(wǎng)絡(luò)的輸出，減少網(wǎng)絡(luò)對無關(guān)信息的依賴。最后，采用基準(zhǔn)數(shù)據(jù)集來測試網(wǎng)絡(luò)的性能，實驗結(jié)果表明STSR在降低模型參數(shù)量的前提下仍然保持較好的重建效果。
A lightweight image super resolution method based on a hybrid CNN-Transformer architecture
Lin Chenghao, Wu Lijun
School of Physics and Information Engineering, Fuzhou University
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 superresolution; lightweighting; Convolutional Neural Network; Transformer
