Article(id=1322961075985056718, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, articleNumber=null, orderNo=null, doi=10.11834/jig.250519, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762272000000, receivedDateStr=2025-11-05, revisedDate=1766073600000, revisedDateStr=2025-12-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348099942, onlineDateStr=2026-10-30, pubDate=1786809600000, pubDateStr=2026-08-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348099942, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348099942, creator=13701087609, updateTime=1793348099942, updator=13701087609, issue=Issue{id=1322960856543273127, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='8', pageStart='2687', pageEnd='3074', issueExtLink='null', onlineDate='null', pubDate='1786809600000', pubDateStr='2026-08-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793348047624, creator='13701087609', updateTime=1793348605344, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963195874378645, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963195874378646, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2897, endPage=2910, ext={EN=ArticleExt(id=1322961078086403023, articleId=1322961075985056718, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Lightweight image super-resolution network with sparse and permuted self-attention, columnId=1322960981189603998, journalTitle=Journal of Image and Graphics, columnName=Image Processing and Coding, runingTitle=null, highlight=null, articleAbstract=
Objective Image super-resolution （SR） is a fundamental low-level vision task in computer vision that aims to reconstruct high-resolution images from low-resolution inputs，thereby enhancing image detail quality and sharpness. High-quality reconstruction not only improves human visual perception but also provides clear and highly structured input features for high-level vision tasks，such as object detection，semantic segmentation，and face recognition. Therefore，SR technology has broad application prospects in intelligent surveillance，medical imaging，autonomous driving，satellite remote sensing，and related fields. Convolutional neural networks （CNNs） have been widely used for SR because of their ability to capture local textures and edges. However，their limited receptive fields hinder the modeling of long-range dependencies，which are essential for preserving global structural consistency. Transformer-based methods，by contrast，leverage self-attention mechanisms to capture the global context，achieving superior reconstruction performance. Despite this advantage，conventional Transformers suffer from high computational costs and redundant attention weight，limiting their applicability in lightweight or real-time scenarios. To address these challenges，we propose a lightweight SR network that integrates the advantages of CNN and Transformer architectures. The network integrates efficient local feature extraction with global context modeling，achieving balance between reconstruction quality and computational efficiency.
Method We propose SPSANet，a lightweight image SR network that is based on sparse and permuted self-attention （SPSA） and composed of shallow feature extraction，deep feature extraction，and high-resolution reconstruction. At the shallow feature extraction stage，a standard 3 × 3 convolution captures basic texture information，and shallow features are forwarded to the reconstruction stage via a long skip connection to provide residual guidance. The deep feature extraction stage consists of four SPSA groups，each containing six SPSA blocks and a convolutional layer. Each block integrates an SPSA module （SPSAM） that expands the receptive field while reducing attention redundancy，a high-frequency enhancement module （HFEM） for refining texture and edge details，and a dual-branch feature fusion module （DBFFM） that effectively fuses global and local features through spatial–channel interactions. Then，shallow and deep features are fused via a global residual connection，and a pixel shuffle operation generates the final high-resolution output. Our method is implemented on the PyTorch framework and trained using an NVIDIA RTX 3090 GPU. The training dataset is DIV2K，which contains 800 images. For the ×2 SR task，the model is trained for a total of 500 K iterations by using the Adam optimizer with an initial learning rate of 2 × 10⁻⁴. The input image patch size is fixed at 64 × 64，the batch size is set to 16，and the MultiStepLR scheduler is applied to halve the learning rate at iterations ［250 K，400 K，450 K，475 K］. For the ×3 and ×4 SR tasks，the model is initialized with the pretrained weights from the ×2 model，and the total number of training iterations is reduced by half.
Result The proposed method is evaluated on five public benchmark datasets and compared with 11 state-of-the-art （SOTA） SR approaches. Experimental results demonstrate that under the constraint of lightweight design，SPSANet achieves either the best or second-best performance across different upscale factors and datasets，showing strong generalization ability and stability. Specifically，when the upscale factor is ×3，SPSANet surpasses the latest SOTA methods by 0.15 dB and 0.25 dB in peak signal-to-noise ratio （PSNR） on Urban100 and Manga109 datasets，respectively，and achieves corresponding improvements in the structural similarity index measure （SSIM）. Furthermore，when the self-ensemble strategy is applied during the testing phase，the model’s performance is further enhanced. Under the same scaling factor，PSNR increases by an additional 0.11 dB and 0.23 dB on Urban100 and Manga109，respectively，indicating that the proposed method maintains strong robustness under multiview inference. Ablation studies are conducted on SPSAM，HFEM，and DBFFM to further investigate the contribution of each component. The results show that removing any of these modules leads to a noticeable degradation in performance. SPSAM plays a key role in global dependency modeling and attention sparsification，HFEM effectively enhances texture restoration quality，and DBFFM substantially improves the fusion of global and local features. In terms of visual quality，SPSANet can reconstruct clear，natural，and sharp image textures，particularly in scenes with complex structures and abundant details. It preserves the structural consistency and perceptual realism of the original images more effectively than existing approaches do.
Conclusion In this work，we propose SPSANet，a lightweight image SR network that integrates an SPSA mechanism to efficiently capture long-range dependencies while minimizing redundant computations. By establishing interlayer attention connections，SPSANet enables focused feature learning，ensuring that the network prioritizes tokens that are the most critical to the reconstruction process. Moreover，the proposed HFEM enhances the recovery of fine textures and structural details，and DBFFM effectively aligns and integrates global and local representations. Through extensive experiments，SPSANet demonstrates superior reconstruction performance and visual fidelity compared with existing lightweight SR approaches，especially in challenging scenarios with intricate patterns and rich textures. Despite these promising results，opportunities for further improvement remain. Future work could enhance model compactness and generalization through knowledge distillation，hybrid convolution-Transformer integration，and adaptation to broad low-level vision tasks （e.g.，image denoising and deblurring）. Overall，SPSANet provides a practical，interpretable solution for lightweight Transformer-based image restoration，offering insights for the design of efficient and high-performing SR networks.
, authors=Siqi Wu, Wei Liu
*, Weidong Chen, authorsList=Siqi Wu, Wei Liu, Weidong Chen, authorCompany=null, correspAuthors=Wei Liu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322961088186287076, articleId=1322961075985056718, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=轻量级稀疏置换自注意力图像超分辨率网络, columnId=1322960981353181856, journalTitle=中国图象图形学报, columnName=图像处理和编码, runingTitle=null, highlight=null, articleAbstract=
目的 图像超分辨重建是计算机视觉领域一个典型低层视觉任务，能够为目标检测、图像分割等高层任务提供更清晰更结构化的输入。基于卷积神经网络（convolutional neural network，CNN）的图像超分辨率模型注重恢复图像的纹理和边缘信息，而基于Transformer的方法能建模全局上下文信息，但是存在注意力权重冗余问题。针对这两种模型的优缺点，设计了一种轻量级图像超分辨率网络。
方法 首先改进了传统的Transformer，提出了一种稀疏置换自注意力机制，在扩大窗口的同时解决冗余问题。在此基础上，基于CNN构建高频信息增强模块加强模型对局部细节信息的重建。在得到两种结构提取的特征后，提出一种双分支特征融合模块对全局特征和局部特征进行高效融合。
结果 本文方法在5个公开数据集上与11种先进超分辨方法进行了对比实验。结果表明，在保证模型轻量化的前提下，稀疏置换自注意力网络（sparse and permuted self-attention network，SPSANet）在不同放大倍率和数据集上均取得最优或次优性能。当放大倍率为3时，在Urban100和Manga109数据集上的峰值信噪比（peak signal-to-noise ratio，PSNR）分别较最新的SOTA（state of the art）方法提升了0.15 dB和0.28 dB。主观视觉效果显示，SPSANet在复杂纹理和细节丰富的场景中重建的图像更加清晰、自然。
结论 本文提出的轻量级稀疏置换自注意力图像超分辨率网络能够在保持较低参数量与计算复杂度的同时，在多个数据集上取得优异的重建效果，展现出良好的泛化性与应用价值。
, authors=吴思琪, 柳薇
*, 陈卫东, authorsList=吴思琪, 柳薇, 陈卫东, authorCompany=null, correspAuthors=柳薇, authorNote=
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柳薇，通信作者，女，副教授，主要研究方向为多媒体信息处理、机器学习。E-mail： liuwei@m.scnu.edu.cn
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柳薇，通信作者，女，副教授，主要研究方向为多媒体信息处理、机器学习。E-mail： liuwei@m.scnu.edu.cn
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2024a. Transcending the limit of local window： advanced super-resolution transformer with adaptive token dictionary//Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle，USA： IEEE： 2856-2865 ［DOI：
10.1109/CVPR52733.2024.00276］, articleTitle=Transcending the limit of local window： advanced super-resolution transformer with adaptive token dictionary, refAbstract=null), Reference(id=1322961140610891846, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, doi=null, pmid=null, pmcid=null, year=2024b, volume=null, issue=null, pageStart=483, pageEnd=500, url=null, language=null, rfNumber=null, rfOrder=36, authorNames=Zhang X, Zhang Y L, Yu F, journalName=null, refType=null, unstructuredReference=
Zhang X，
Zhang Y L and
Yu F.
2024b. HiT-SR： hierarchical transformer for efficient image super-resolution//Proceedings of the 18th European Conference on Computer Vision. Milan，Italy： Springer： 483-500 ［DOI：
10.1007/978-3-031-73661-2_27］, articleTitle=HiT-SR： hierarchical transformer for efficient image super-resolution, refAbstract=null), Reference(id=1322961141055488071, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=null, pageStart=649, pageEnd=667, url=null, language=null, rfNumber=null, rfOrder=37, authorNames=Zhang X D, Zeng H, Guo S, Zhang L, journalName=null, refType=null, unstructuredReference=
Zhang X D，
Zeng H，
Guo S and
Zhang L.
2022. Efficient long-range attention network for image super-resolution//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv，Israel： Springer： 649-667 ［DOI：
10.1007/978-3-031-19790-1_39］, articleTitle=Efficient long-range attention network for image super-resolution, refAbstract=null), Reference(id=1322961141432975432, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=12734, pageEnd=12745, url=null, language=null, rfNumber=null, rfOrder=38, authorNames=Zhou Y P, Li Z, Guo C L, Bai S, Chen M M, Hou Q B, journalName=null, refType=null, unstructuredReference=
Zhou Y P，
Li Z，
Guo C L，
Bai S，
Chen M M and
Hou Q B.
2023. SRFormer： permuted self-attention for single image super-resolution//Proceedings of 2023 IEEE/CVF International Conference on Computer Vision. Paris，France： IEEE： 12734-12745 ［DOI：
10.1109/ICCV51070.2023.01174］, articleTitle=SRFormer： permuted self-attention for single image super-resolution, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322961089037730790, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, xref=null, ext=[AuthorCompanyExt(id=1322961089050313703, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, companyId=1322961089037730790, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Computer Science，South China Normal University，Guangzhou 510631，China), AuthorCompanyExt(id=1322961089062896616, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, companyId=1322961089037730790, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=华南师范大学计算机学院，广州 510631)])], figs=[ArticleFig(id=1322961100374933505, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.1, caption=
Cosine similarity calculated based on Urban100（（a）×2 cosine similarity；（b）×4 cosine similarity）, figureFileSmall=LuYdFi6ACaEOwpsvi8UKPw==, figureFileBig=NR/UKv1ruaEZG89kCK/teA==, tableContent=null), ArticleFig(id=1322961100739837954, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图1, caption=
基于Urban100计算的余弦相似度, figureFileSmall=LuYdFi6ACaEOwpsvi8UKPw==, figureFileBig=NR/UKv1ruaEZG89kCK/teA==, tableContent=null), ArticleFig(id=1322961101993934852, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.2, caption=
Network architecture, figureFileSmall=P1RvaP4wd1t7HCj6fUPWuQ==, figureFileBig=wIzLSzDr2uYTa6ylfs/MRQ==, tableContent=null), ArticleFig(id=1322961102895710213, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图2, caption=
网络结构, figureFileSmall=P1RvaP4wd1t7HCj6fUPWuQ==, figureFileBig=wIzLSzDr2uYTa6ylfs/MRQ==, tableContent=null), ArticleFig(id=1322961104518905862, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.3, caption=
Sparse permuted self-attention block（（a）SPSA；（b）high-freguency in formation enhancement；（c）DBFF；（d）C-I；（e）S-I）, figureFileSmall=MKfTXTuzTOm69ahXaIGLZg==, figureFileBig=cdRCZ51VDI3nLmB19q7HXg==, tableContent=null), ArticleFig(id=1322961105374543879, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图3, caption=
稀疏置换注意力块, figureFileSmall=MKfTXTuzTOm69ahXaIGLZg==, figureFileBig=cdRCZ51VDI3nLmB19q7HXg==, tableContent=null), ArticleFig(id=1322961105777197064, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.4, caption=
Compression permutation operation, figureFileSmall=jqtS2x1s0G11/xtz5NX91A==, figureFileBig=VS9EaokJF9UITNwFnkbw7Q==, tableContent=null), ArticleFig(id=1322961106192433161, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图4, caption=
压缩置换操作, figureFileSmall=jqtS2x1s0G11/xtz5NX91A==, figureFileBig=VS9EaokJF9UITNwFnkbw7Q==, tableContent=null), ArticleFig(id=1322961106595086346, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.5, caption=
Comparison of super-resolution visualization at different magnifications on Urban100, figureFileSmall=VIQK2wkG322OG/CLus7EAg==, figureFileBig=RGSeutp2CYXkI6S34xm1mA==, tableContent=null), ArticleFig(id=1322961107043876875, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图5, caption=
Urban100上不同放大倍数超分辨率可视化对比, figureFileSmall=VIQK2wkG322OG/CLus7EAg==, figureFileBig=RGSeutp2CYXkI6S34xm1mA==, tableContent=null), ArticleFig(id=1322961108721598476, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.6, caption=
Comparison of entropy values at each layer of different attention mechanisms, figureFileSmall=ECAKiTticPtkRpeiyEhh1w==, figureFileBig=zDbMDUdJuFd9iMS5HE3hYA==, tableContent=null), ArticleFig(id=1322961109195554829, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图6, caption=
不同注意力机制各层的熵值对比, figureFileSmall=ECAKiTticPtkRpeiyEhh1w==, figureFileBig=zDbMDUdJuFd9iMS5HE3hYA==, tableContent=null), ArticleFig(id=1322961109975695374, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.7, caption=
Comparison of feature maps of different branches （（a） LR image； （b） feature map of Conv； （c） feature map of Transformer）, figureFileSmall=0cDgmTPi4vFAHJNV9CCHcQ==, figureFileBig=AiTDglr5S2uJW1xX2qY7hA==, tableContent=null), ArticleFig(id=1322961110424485903, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图7, caption=
不同分支特征图对比, figureFileSmall=0cDgmTPi4vFAHJNV9CCHcQ==, figureFileBig=AiTDglr5S2uJW1xX2qY7hA==, tableContent=null), ArticleFig(id=1322961110818750480, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Fig.8, caption=
LAM comparison results, figureFileSmall=y64RiIkp8DLMMZ76zyFVuQ==, figureFileBig=HFMkMCV6HciR/SD5akM6pg==, tableContent=null), ArticleFig(id=1322961111242375185, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=图8, caption=
LAM比较结果, figureFileSmall=y64RiIkp8DLMMZ76zyFVuQ==, figureFileBig=HFMkMCV6HciR/SD5akM6pg==, tableContent=null), ArticleFig(id=1322961111670194194, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Table 1, caption=
Results of objective results of ×2 super-resolution for different methods, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 370 | 316.3 | 37.99 | 0.960 4 | 33.57 | 0.917 5 | 32.16 | 0.899 4 | 31.98 | 0.927 2 | 38.54 | 0.976 9 |
| CARN（Ahn等，2018） | 1 592 | 222.8 | 37.76 | 0.959 0 | 33.52 | 0.916 6 | 32.09 | 0.897 8 | 31.92 | 0.925 6 | 38.36 | 0.976 5 |
| IMDN（Hui等，2019） | 694 | 158.8 | 38.00 | 0.960 5 | 33.63 | 0.917 7 | 32.19 | 0.899 6 | 32.17 | 0.928 3 | 38.88 | 0.977 4 |
| LatticeNet（Luo等，2020） | 756 | 169.5 | 38.06 | 0.960 7 | 33.70 | 0.918 7 | 32.20 | 0.899 9 | 32.25 | 0.928 8 | - | - |
| SwinIR-It（Liang等，2021） | 878 | 195.6 | 38.14 | 0.961 1 | 33.86 | 0.920 6 | 32.31 | 0.901 2 | 32.76 | 0.934 0 | 39.12 | 0.978 3 |
| LBNet（Gao等，2022） | 731 | 153.2 | 38.05 | 0.960 7 | 33.65 | 0.917 7 | 32.16 | 0.899 4 | 32.30 | 0.929 1 | 38.88 | 0.977 5 |
| ESRT（Lu等，2022） | 677 | 191.4 | 38.03 | 0.960 0 | 33.75 | 0.918 4 | 32.25 | 0.900 1 | 32.58 | 0.931 8 | 39.12 | 0.977 4 |
| SwinIR-NG（Choi等，2023） | 1 181 | 274.1 | 38.17 | 0.961 2 | 33.94 | 0.920 5 | 32.31 | 0.901 3 | 32.78 | 0.934 0 | 39.20 | 0.978 5 |
| SRFormer-It（Zhou等，2023） | 853 | 197.5 | 38.23 | 0.961 3 | 33.94 | 0.920 9 | 32.36 | 0.901 9 | 32.91 | 0.935 3 | 39.28 | 0.978 5 |
| BSTN（毕修平 等，2024） | 736 | 163.6 | 38.17 | 0.961 6 | 33.87 | 0.920 1 | 32.28 | 0.901 0 | 32.60 | 0.931 8 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 071 | - | 38.15 | 0.961 2 | 33.85 | 0.919 7 | 32.33 | 0.901 3 | 32.58 | 0.933 1 | 39.06 | 0.978 2 |
| SPSANet（本文） | 1 063 | 247.0 | 38.25 | 0.961 6 | 33.95 | 0.921 4 | 32.35 | 0.902 0 | 32.99 | 0.936 3 | 39.34 | 0.978 7 |
| SPSANet+（本文） | 1 063 | 247.0 | 38.28 | 0.961 7 | 34.02 | 0.921 7 | 32.38 | 0.902 3 | 33.14 | 0.937 2 | 39.48 | 0.979 0 |
), ArticleFig(id=1322961113322749972, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=表1, caption=
不同方法的2倍超分辨率客观评价结果
, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 370 | 316.3 | 37.99 | 0.960 4 | 33.57 | 0.917 5 | 32.16 | 0.899 4 | 31.98 | 0.927 2 | 38.54 | 0.976 9 |
| CARN（Ahn等，2018） | 1 592 | 222.8 | 37.76 | 0.959 0 | 33.52 | 0.916 6 | 32.09 | 0.897 8 | 31.92 | 0.925 6 | 38.36 | 0.976 5 |
| IMDN（Hui等，2019） | 694 | 158.8 | 38.00 | 0.960 5 | 33.63 | 0.917 7 | 32.19 | 0.899 6 | 32.17 | 0.928 3 | 38.88 | 0.977 4 |
| LatticeNet（Luo等，2020） | 756 | 169.5 | 38.06 | 0.960 7 | 33.70 | 0.918 7 | 32.20 | 0.899 9 | 32.25 | 0.928 8 | - | - |
| SwinIR-It（Liang等，2021） | 878 | 195.6 | 38.14 | 0.961 1 | 33.86 | 0.920 6 | 32.31 | 0.901 2 | 32.76 | 0.934 0 | 39.12 | 0.978 3 |
| LBNet（Gao等，2022） | 731 | 153.2 | 38.05 | 0.960 7 | 33.65 | 0.917 7 | 32.16 | 0.899 4 | 32.30 | 0.929 1 | 38.88 | 0.977 5 |
| ESRT（Lu等，2022） | 677 | 191.4 | 38.03 | 0.960 0 | 33.75 | 0.918 4 | 32.25 | 0.900 1 | 32.58 | 0.931 8 | 39.12 | 0.977 4 |
| SwinIR-NG（Choi等，2023） | 1 181 | 274.1 | 38.17 | 0.961 2 | 33.94 | 0.920 5 | 32.31 | 0.901 3 | 32.78 | 0.934 0 | 39.20 | 0.978 5 |
| SRFormer-It（Zhou等，2023） | 853 | 197.5 | 38.23 | 0.961 3 | 33.94 | 0.920 9 | 32.36 | 0.901 9 | 32.91 | 0.935 3 | 39.28 | 0.978 5 |
| BSTN（毕修平 等，2024） | 736 | 163.6 | 38.17 | 0.961 6 | 33.87 | 0.920 1 | 32.28 | 0.901 0 | 32.60 | 0.931 8 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 071 | - | 38.15 | 0.961 2 | 33.85 | 0.919 7 | 32.33 | 0.901 3 | 32.58 | 0.933 1 | 39.06 | 0.978 2 |
| SPSANet（本文） | 1 063 | 247.0 | 38.25 | 0.961 6 | 33.95 | 0.921 4 | 32.35 | 0.902 0 | 32.99 | 0.936 3 | 39.34 | 0.978 7 |
| SPSANet+（本文） | 1 063 | 247.0 | 38.28 | 0.961 7 | 34.02 | 0.921 7 | 32.38 | 0.902 3 | 33.14 | 0.937 2 | 39.48 | 0.979 0 |
), ArticleFig(id=1322961113805094933, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Table 2, caption=
Results of objective results of ×3 super-resolution for different methods, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 555 | 160.2 | 34.37 | 0.927 0 | 30.28 | 0.841 7 | 29.09 | 0.805 2 | 28.15 | 0.852 7 | 33.45 | 0.943 9 |
| CARN（Ahn等，2018） | 1 592 | 118.8 | 34.29 | 0.925 5 | 30.29 | 0.840 7 | 29.06 | 0.803 4 | 28.06 | 0.849 3 | 33.50 | 0.944 0 |
| IMDN（Hui等，2019） | 703 | 71.5 | 34.36 | 0.927 0 | 30.32 | 0.841 7 | 29.09 | 0.804 6 | 28.17 | 0.851 9 | 33.61 | 0.944 5 |
| LatticeNet（Luo等，2020） | 765 | 76.3 | 34.53 | 0.928 1 | 30.39 | 0.842 4 | 29.15 | 0.805 9 | 28.33 | 0.853 8 | - | - |
| SwinIR-It（Liang等，2021） | 886 | 87.2 | 34.62 | 0.928 9 | 30.54 | 0.846 3 | 29.20 | 0.808 2 | 28.66 | 0.862 4 | 33.98 | 0.947 8 |
| LBNet（Gao等，2022） | 736 | 68.4 | 34.47 | 0.927 7 | 30.38 | 0.841 7 | 29.13 | 0.806 1 | 28.42 | 0.855 9 | 33.82 | 0.946 0 |
| ESRT（Lu等，2022） | 770 | 96.4 | 34.42 | 0.926 8 | 30.43 | 0.843 3 | 29.15 | 0.806 3 | 28.46 | 0.857 4 | 33.95 | 0.945 5 |
| SwinIR-NG（Choi等，2023） | 1 190 | 114.1 | 34.64 | 0.929 3 | 30.58 | 0.847 1 | 29.24 | 0.809 0 | 28.75 | 0.863 9 | 34.22 | 0.948 8 |
| SRFormer-It（Zhou等，2023） | 862 | 87.8 | 34.67 | 0.929 6 | 30.57 | 0.846 9 | 29.26 | 0.809 9 | 28.81 | 0.865 5 | 34.19 | 0.948 9 |
| BSTN（毕修平 等，2024） | 736 | 72.2 | 34.68 | 0.929 6 | 30.54 | 0.845 1 | 29.23 | 0.808 1 | 28.62 | 0.860 4 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 079 | - | 34.63 | 0.929 2 | 30.55 | 0.846 4 | 29.24 | 0.807 7 | 28.69 | 0.862 7 | 34.05 | 0.948 1 |
| SPSANet（本文） | 1 071 | 108.8 | 34.67 | 0.930 0 | 30.56 | 0.848 4 | 29.27 | 0.810 3 | 28.96 | 0.868 9 | 34.47 | 0.950 0 |
| SPSANet+（本文） | 1 071 | 108.8 | 34.76 | 0.930 5 | 30.57 | 0.849 2 | 29.32 | 0.811 1 | 29.10 | 0.870 7 | 34.64 | 0.951 0 |
), ArticleFig(id=1322961114237108246, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=表2, caption=
不同方法的3倍超分辨率客观评价结果
, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 555 | 160.2 | 34.37 | 0.927 0 | 30.28 | 0.841 7 | 29.09 | 0.805 2 | 28.15 | 0.852 7 | 33.45 | 0.943 9 |
| CARN（Ahn等，2018） | 1 592 | 118.8 | 34.29 | 0.925 5 | 30.29 | 0.840 7 | 29.06 | 0.803 4 | 28.06 | 0.849 3 | 33.50 | 0.944 0 |
| IMDN（Hui等，2019） | 703 | 71.5 | 34.36 | 0.927 0 | 30.32 | 0.841 7 | 29.09 | 0.804 6 | 28.17 | 0.851 9 | 33.61 | 0.944 5 |
| LatticeNet（Luo等，2020） | 765 | 76.3 | 34.53 | 0.928 1 | 30.39 | 0.842 4 | 29.15 | 0.805 9 | 28.33 | 0.853 8 | - | - |
| SwinIR-It（Liang等，2021） | 886 | 87.2 | 34.62 | 0.928 9 | 30.54 | 0.846 3 | 29.20 | 0.808 2 | 28.66 | 0.862 4 | 33.98 | 0.947 8 |
| LBNet（Gao等，2022） | 736 | 68.4 | 34.47 | 0.927 7 | 30.38 | 0.841 7 | 29.13 | 0.806 1 | 28.42 | 0.855 9 | 33.82 | 0.946 0 |
| ESRT（Lu等，2022） | 770 | 96.4 | 34.42 | 0.926 8 | 30.43 | 0.843 3 | 29.15 | 0.806 3 | 28.46 | 0.857 4 | 33.95 | 0.945 5 |
| SwinIR-NG（Choi等，2023） | 1 190 | 114.1 | 34.64 | 0.929 3 | 30.58 | 0.847 1 | 29.24 | 0.809 0 | 28.75 | 0.863 9 | 34.22 | 0.948 8 |
| SRFormer-It（Zhou等，2023） | 862 | 87.8 | 34.67 | 0.929 6 | 30.57 | 0.846 9 | 29.26 | 0.809 9 | 28.81 | 0.865 5 | 34.19 | 0.948 9 |
| BSTN（毕修平 等，2024） | 736 | 72.2 | 34.68 | 0.929 6 | 30.54 | 0.845 1 | 29.23 | 0.808 1 | 28.62 | 0.860 4 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 079 | - | 34.63 | 0.929 2 | 30.55 | 0.846 4 | 29.24 | 0.807 7 | 28.69 | 0.862 7 | 34.05 | 0.948 1 |
| SPSANet（本文） | 1 071 | 108.8 | 34.67 | 0.930 0 | 30.56 | 0.848 4 | 29.27 | 0.810 3 | 28.96 | 0.868 9 | 34.47 | 0.950 0 |
| SPSANet+（本文） | 1 071 | 108.8 | 34.76 | 0.930 5 | 30.57 | 0.849 2 | 29.32 | 0.811 1 | 29.10 | 0.870 7 | 34.64 | 0.951 0 |
), ArticleFig(id=1322961114979500055, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Table 3, caption=
Results of objective results of ×4 super-resolution for different methods, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 518 | 114.0 | 32.09 | 0.893 8 | 28.58 | 0.781 3 | 27.57 | 0.735 7 | 26.04 | 0.784 9 | 30.35 | 0.906 7 |
| CARN（Ahn等，2018） | 1 592 | 90.0 | 32.13 | 0.893 7 | 28.60 | 0.780 6 | 27.58 | 0.734 9 | 26.07 | 0.783 7 | 30.47 | 0.908 4 |
| IMDN（Hui等，2019） | 752 | 40.9 | 32.21 | 0.894 8 | 28.58 | 0.781 1 | 27.56 | 0.735 3 | 26.04 | 0.783 8 | 30.45 | 0.907 5 |
| LatticeNet（Luo等，2020） | 777 | 43.6 | 32.18 | 0.894 3 | 28.61 | 0.781 2 | 27.57 | 0.735 5 | 26.14 | 0.784 4 | - | - |
| SwinIR-It（Liang等，2021） | 897 | 49.6 | 32.44 | 0.897 6 | 28.77 | 0.785 8 | 27.69 | 0.740 6 | 26.47 | 0.798 0 | 30.92 | 0.915 1 |
| LBNet（Gao等，2022） | 742 | 38.9 | 32.29 | 0.896 0 | 28.68 | 0.783 2 | 27.62 | 0.738 2 | 26.27 | 0.790 6 | 30.76 | 0.911 1 |
| ESRT（Lu等，2022） | 751 | 67.7 | 32.19 | 0.894 7 | 28.69 | 0.783 3 | 27.69 | 0.737 9 | 26.39 | 0.796 2 | 30.75 | 0.910 0 |
| SwinIR-NG（Choi等，2023） | 1 201 | 63.0 | 32.44 | 0.898 0 | 28.83 | 0.787 0 | 27.73 | 0.741 8 | 26.61 | 0.801 0 | 31.09 | 0.916 1 |
| SRFormer-It（Zhou等，2023） | 873 | 52.7 | 32.51 | 0.898 8 | 28.82 | 0.787 2 | 27.73 | 0.742 2 | 26.67 | 0.803 2 | 31.17 | 0.916 5 |
| BSTN（毕修平 等，2024） | 751 | 41.8 | 32.50 | 0.898 5 | 28.78 | 0.785 2 | 27.71 | 0.740 3 | 26.49 | 0.796 5 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 071 | - | 32.37 | 0.897 5 | 28.85 | 0.786 4 | 27.73 | 0.741 0 | 26.52 | 0.798 5 | 30.95 | 0.915 3 |
| SPSANet（本文） | 1 082 | 65.1 | 32.39 | 0.899 2 | 28.70 | 0.788 8 | 27.74 | 0.743 2 | 26.80 | 0.808 3 | 31.33 | 0.918 3 |
| SPSANet+（本文） | 1 082 | 65.1 | 32.51 | 0.900 2 | 28.72 | 0.789 9 | 27.79 | 0.744 1 | 26.91 | 0.810 3 | 31.56 | 0.920 2 |
), ArticleFig(id=1322961115084357656, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=表3, caption=
不同方法的4倍超分辨率客观评价结果
, figureFileSmall=null, figureFileBig=null, tableContent=
| 方法 | 参数量/k | Mult- Adds/G | Set5 | Set14 | BSD100 | Urban100 | Manage109 |
|---|
PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM | PSNR /dB | SSIM |
|---|
| EDSR（Lim等，2017） | 1 518 | 114.0 | 32.09 | 0.893 8 | 28.58 | 0.781 3 | 27.57 | 0.735 7 | 26.04 | 0.784 9 | 30.35 | 0.906 7 |
| CARN（Ahn等，2018） | 1 592 | 90.0 | 32.13 | 0.893 7 | 28.60 | 0.780 6 | 27.58 | 0.734 9 | 26.07 | 0.783 7 | 30.47 | 0.908 4 |
| IMDN（Hui等，2019） | 752 | 40.9 | 32.21 | 0.894 8 | 28.58 | 0.781 1 | 27.56 | 0.735 3 | 26.04 | 0.783 8 | 30.45 | 0.907 5 |
| LatticeNet（Luo等，2020） | 777 | 43.6 | 32.18 | 0.894 3 | 28.61 | 0.781 2 | 27.57 | 0.735 5 | 26.14 | 0.784 4 | - | - |
| SwinIR-It（Liang等，2021） | 897 | 49.6 | 32.44 | 0.897 6 | 28.77 | 0.785 8 | 27.69 | 0.740 6 | 26.47 | 0.798 0 | 30.92 | 0.915 1 |
| LBNet（Gao等，2022） | 742 | 38.9 | 32.29 | 0.896 0 | 28.68 | 0.783 2 | 27.62 | 0.738 2 | 26.27 | 0.790 6 | 30.76 | 0.911 1 |
| ESRT（Lu等，2022） | 751 | 67.7 | 32.19 | 0.894 7 | 28.69 | 0.783 3 | 27.69 | 0.737 9 | 26.39 | 0.796 2 | 30.75 | 0.910 0 |
| SwinIR-NG（Choi等，2023） | 1 201 | 63.0 | 32.44 | 0.898 0 | 28.83 | 0.787 0 | 27.73 | 0.741 8 | 26.61 | 0.801 0 | 31.09 | 0.916 1 |
| SRFormer-It（Zhou等，2023） | 873 | 52.7 | 32.51 | 0.898 8 | 28.82 | 0.787 2 | 27.73 | 0.742 2 | 26.67 | 0.803 2 | 31.17 | 0.916 5 |
| BSTN（毕修平 等，2024） | 751 | 41.8 | 32.50 | 0.898 5 | 28.78 | 0.785 2 | 27.71 | 0.740 3 | 26.49 | 0.796 5 | - | - |
| MLFN-L（宋霄罡 等，2025） | 1 071 | - | 32.37 | 0.897 5 | 28.85 | 0.786 4 | 27.73 | 0.741 0 | 26.52 | 0.798 5 | 30.95 | 0.915 3 |
| SPSANet（本文） | 1 082 | 65.1 | 32.39 | 0.899 2 | 28.70 | 0.788 8 | 27.74 | 0.743 2 | 26.80 | 0.808 3 | 31.33 | 0.918 3 |
| SPSANet+（本文） | 1 082 | 65.1 | 32.51 | 0.900 2 | 28.72 | 0.789 9 | 27.79 | 0.744 1 | 26.91 | 0.810 3 | 31.56 | 0.920 2 |
), ArticleFig(id=1322961115877081114, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=EN, label=Table 4, caption=
Comparison of result with different model settings on Urban100（×2） dataset, figureFileSmall=null, figureFileBig=null, tableContent=
| 基线 | 稀疏置换注意力 | 高频信息增强 | 拼接加卷积 | 双分支特征融合 | 参数量/k | Mult-Adds/G | PSNR/dB | SSIM |
|---|
| √ | - | - | - | - | 853 | 197.5 | 32.52 | 0.932 2 |
| √ | √ | - | - | - | 853 | 197.5 | 32.63 | 0.933 2 |
| √ | √ | √ | √ | - | 1 218 | 284.0 | 32.74 | 0.934 0 |
| √ | √ | √ | - | √ | 1 063 | 247.0 | 32.79 | 0.934 3 |
), ArticleFig(id=1322961117961650205, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961075985056718, language=CN, label=表4, caption=
不同模型在Urban100（×2）数据集的结果对比
, figureFileSmall=null, figureFileBig=null, tableContent=
| 基线 | 稀疏置换注意力 | 高频信息增强 | 拼接加卷积 | 双分支特征融合 | 参数量/k | Mult-Adds/G | PSNR/dB | SSIM |
|---|
| √ | - | - | - | - | 853 | 197.5 | 32.52 | 0.932 2 |
| √ | √ | - | - | - | 853 | 197.5 | 32.63 | 0.933 2 |
| √ | √ | √ | √ | - | 1 218 | 284.0 | 32.74 | 0.934 0 |
| √ | √ | √ | - | √ | 1 063 | 247.0 | 32.79 | 0.934 3 |
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