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基于CNN-Transformer混合構架的輕量圖像超分辨率方法
網(wǎng)絡(luò )安全與數據治理
林承浩,吳麗君
福州大學(xué)物理與信息工程學(xué)院
摘要: 針對基于混合構架的圖像超分模型通常需要較高計算成本的問(wèn)題,提出了一種基于CNN-Transformer混合構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模塊(Feature Enhancement Block,FEB),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,設計了一種動(dòng)態(tài)調整模塊(Dynamic Adjustment,DA),使得網(wǎng)絡(luò )能根據輸入圖像來(lái)動(dòng)態(tài)調整網(wǎng)絡(luò )的輸出,減少網(wǎng)絡(luò )對無(wú)關(guān)信息的依賴(lài)。最后,采用基準數據集來(lái)測試網(wǎng)絡(luò )的性能,實(shí)驗結果表明STSR在降低模型參數量的前提下仍然保持較好的重建效果。
中圖分類(lèi)號:TP391文獻標識碼:ADOI:10.19358/j.issn.2097-1788.2024.03.005
引用格式:林承浩,吳麗君.基于CNN-Transformer混合構架的輕量圖像超分辨率方法[J].網(wǎng)絡(luò )安全與數據治理,2024,43(3):27-33.
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 superresolution; lightweighting; Convolutional Neural Network; Transformer

引言

圖像超分辨率(Super Resolution, SR)是一項被廣泛關(guān)注的計算機視覺(jué)任務(wù),其目的是從低分辨率(Low Resolution, LR)圖像中重建出高質(zhì)量的高分辨率(High Resolution, HR)圖像[1]。由于建出高質(zhì)量的高分辨率圖像具有不適定的性質(zhì),因此極具挑戰性[2]。隨著(zhù)深度學(xué)習等新興技術(shù)的崛起,許多基于卷積神經(jīng)網(wǎng)絡(luò )(CNN)的方法被引入到圖像超分任務(wù)中[3-6]。SRCNN[3]首次將卷積神經(jīng)網(wǎng)絡(luò )引入到圖像超分任務(wù)中,用卷積神經(jīng)網(wǎng)絡(luò )來(lái)學(xué)習圖像的特征表示,并通過(guò)卷積層的堆疊來(lái)逐步提取更高級別的特征,使得重建出的圖像具有較高的質(zhì)量。在后續研究中,Kaiming He等人提出了殘差結構ResNet[5],通過(guò)引入跳躍連接,允許梯度能夠跨越層進(jìn)行傳播,有助于減輕梯度消失的問(wèn)題,使得模型在較深的網(wǎng)絡(luò )情況下仍然能保持較好的性能。Bee Lim等人在EDSR[6]中也引入了殘差結構,EDSR實(shí)際上是SRResnet[7]的改進(jìn)版,去除了傳統殘差網(wǎng)絡(luò )中的BN層,在節省下來(lái)的空間中擴展模型尺寸來(lái)增強表現力。RCAN[8]中提出了一種基于Residual in Residual結構(RIR)和通道注意力機制(CA)的深度殘差網(wǎng)絡(luò )。雖然這些模型在當時(shí)取得了較好的效果,但本質(zhì)上都是基于CNN網(wǎng)絡(luò )的模型,網(wǎng)絡(luò )中卷積核的大小會(huì )限制可以檢測的空間范圍,導致無(wú)法捕捉到長(cháng)距離的依賴(lài)關(guān)系,意味著(zhù)它們只能提取到局部特征,無(wú)法獲取全局的信息,不利于紋理細節的恢復,使得圖像重建的效果不佳[5]。


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作者信息:

林承浩,吳麗君

福州大學(xué)物理與信息工程學(xué)院,福建福州350108


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