{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T21:58:41Z","timestamp":1785448721508,"version":"3.56.0"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Physics Communications"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.cpc.2026.110306","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:57:21Z","timestamp":1783954641000},"page":"110306","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Turbulent super-resolution reconstruction based on residual convolutional attention mechanism"],"prefix":"10.1016","volume":"327","author":[{"given":"Mengxue","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longye","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.cpc.2026.110306_bib0001","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1088\/0957-0233\/12\/11\/705","article-title":"Turbulent flows","volume":"12","author":"Pope","year":"2001","journal-title":"Meas. Sci. Technol."},{"issue":"1","key":"10.1016\/j.cpc.2026.110306_bib0002","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1146\/annurev-fluid-010719-060214","article-title":"Machine learning for fluid mechanics","volume":"52","author":"Brunton","year":"2020","journal-title":"Annu. Rev. Fluid Mech."},{"issue":"1","key":"10.1016\/j.cpc.2026.110306_bib0003","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1146\/annurev-fluid-120710-101204","article-title":"Particle image velocimetry for complex and turbulent flows","volume":"45","author":"Westerweel","year":"2013","journal-title":"Annu. Rev. Fluid Mech."},{"key":"10.1016\/j.cpc.2026.110306_bib0004","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1017\/jfm.2015.268","article-title":"Direct numerical simulation of turbulent channel flow up to Re\u03c4\u202f\u2248\u202f5200","volume":"774","author":"Lee","year":"2015","journal-title":"J. Fluid Mech."},{"key":"10.1016\/j.cpc.2026.110306_bib0005","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.sigpro.2016.05.002","article-title":"Image super-resolution: the techniques, applications, and future","volume":"128","author":"Yue","year":"2016","journal-title":"Signal Process."},{"issue":"6","key":"10.1016\/j.cpc.2026.110306_bib0006","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"10.1016\/j.cpc.2026.110306_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlaseng.2020.106090","article-title":"Bicubic interpolation and extrapolation iteration method for high resolution digital holographic reconstruction","volume":"130","author":"Huang","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"10.1016\/j.cpc.2026.110306_bib0008","series-title":"2008 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"PSF estimation using sharp edge prediction","author":"Joshi","year":"2008"},{"key":"10.1016\/j.cpc.2026.110306_bib0009","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"945","article-title":"Nonparametric blind super-resolution","author":"Michaeli","year":"2013"},{"key":"10.1016\/j.cpc.2026.110306_bib0010","series-title":"Space-Time Super-Resolution From a Single Video","first-page":"3353","author":"Shahar","year":"2011"},{"key":"10.1016\/j.cpc.2026.110306_bib0011","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1059","article-title":"Fast image super-resolution based on in-place example regression","author":"Yang","year":"2013"},{"issue":"3","key":"10.1016\/j.cpc.2026.110306_bib0012","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: a technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.cpc.2026.110306_bib0013","doi-asserted-by":"crossref","unstructured":"M. Bevilacqua, A. Roumy, C. Guillemot, M.L. Alberi-Morel, Low-complexity single-image super-resolution based on nonnegative neighbor embedding, in: Proceedings British Machine Vision Conference, 2012, pp. 135.1\u2013135.10.","DOI":"10.5244\/C.26.135"},{"key":"10.1016\/j.cpc.2026.110306_bib0014","series-title":"Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.","article-title":"Super-resolution through neighbor embedding","volume":"vol. 1","author":"Chang","year":"2004"},{"issue":"2","key":"10.1016\/j.cpc.2026.110306_bib0015","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-based super-resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"key":"10.1016\/j.cpc.2026.110306_bib0016","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.infrared.2015.06.017","article-title":"A novel infrared image super-resolution method based on sparse representation","volume":"71","author":"Zhao","year":"2015","journal-title":"Infrared Phys. Technol."},{"issue":"11","key":"10.1016\/j.cpc.2026.110306_bib0017","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.cpc.2026.110306_bib0018","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1109\/TMM.2014.2364976","article-title":"Fast single image super-resolution via self-example learning and sparse representation","volume":"16","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Multimed."},{"issue":"4","key":"10.1016\/j.cpc.2026.110306_bib0019","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s00162-023-00663-0","article-title":"Super-resolution analysis via machine learning: a survey for fluid flows","volume":"37","author":"Fukami","year":"2023","journal-title":"Theor. Comput. Fluid Dyn."},{"key":"10.1016\/j.cpc.2026.110306_bib0020","series-title":"Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part IV 13","first-page":"184","article-title":"Learning a deep convolutional network for image super-resolution","author":"Dong","year":"2014"},{"key":"10.1016\/j.cpc.2026.110306_bib0021","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3147","article-title":"Image super-resolution via deep recursive residual network","author":"Tai","year":"2017"},{"key":"10.1016\/j.cpc.2026.110306_bib0022","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2472","article-title":"Residual dense network for image super-resolution","author":"Zhang","year":"2018"},{"key":"10.1016\/j.cpc.2026.110306_bib0023","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops","first-page":"136","article-title":"Enhanced deep residual networks for single image super-resolution","author":"Lim","year":"2017"},{"key":"10.1016\/j.cpc.2026.110306_bib0024","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1874","article-title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","author":"Shi","year":"2016"},{"key":"10.1016\/j.cpc.2026.110306_bib0025","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1646","article-title":"Accurate image super-resolution using very deep convolutional networks","author":"Kim","year":"2016"},{"key":"10.1016\/j.cpc.2026.110306_bib0026","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1017\/jfm.2019.238","article-title":"Super-resolution reconstruction of turbulent flows with machine learning","volume":"870","author":"Fukami","year":"2019","journal-title":"J. Fluid Mech."},{"key":"10.1016\/j.cpc.2026.110306_bib0027","doi-asserted-by":"crossref","DOI":"10.1017\/jfm.2020.948","article-title":"Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows","volume":"909","author":"Fukami","year":"2021","journal-title":"J. Fluid Mech."},{"key":"10.1016\/j.cpc.2026.110306_bib0028","series-title":"2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT)","first-page":"331","article-title":"SURFNet: super-resolution of turbulent flows with transfer learning using small datasets","author":"Obiols-Sales","year":"2021"},{"issue":"11","key":"10.1016\/j.cpc.2026.110306_bib0029","doi-asserted-by":"crossref","DOI":"10.1063\/5.0030040","article-title":"Deep learning methods for super-resolution reconstruction of temperature fields in a supersonic combustor","volume":"10","author":"Kong","year":"2020","journal-title":"AIP Adv."},{"issue":"12","key":"10.1016\/j.cpc.2026.110306_bib0030","doi-asserted-by":"crossref","DOI":"10.1063\/1.5127031","article-title":"Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework","volume":"31","author":"Deng","year":"2019","journal-title":"Phys. Fluids"},{"issue":"4","key":"10.1016\/j.cpc.2026.110306_bib0031","first-page":"1","article-title":"TempoGAN: a temporally coherent, volumetric GAN for super-resolution fluid flow","volume":"37","author":"Xie","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.cpc.2026.110306_bib0032","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s00162-021-00593-9","article-title":"Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks","volume":"35","author":"Siddani","year":"2021","journal-title":"Theor. Comput. Fluid Dyn."},{"key":"10.1016\/j.cpc.2026.110306_bib0033","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cpc.2026.110306_bib0034","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","first-page":"38","article-title":"Transformers: state-of-the-art natural language processing","author":"Wolf","year":"2020"},{"key":"10.1016\/j.cpc.2026.110306_bib0035","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12124","article-title":"CSwin transformer: a general vision transformer backbone with cross-shaped windows","author":"Dong","year":"2022"},{"key":"10.1016\/j.cpc.2026.110306_bib0036","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"2731","article-title":"Pale transformer: a general vision transformer backbone with pale-shaped attention","volume":"vol. 36","author":"Wu","year":"2022"},{"issue":"5","key":"10.1016\/j.cpc.2026.110306_bib0037","doi-asserted-by":"crossref","DOI":"10.1063\/5.0149551","article-title":"Super-resolution reconstruction of turbulent flows with a transformer-based deep learning framework","volume":"35","author":"Xu","year":"2023","journal-title":"Phys. Fluids"},{"issue":"6","key":"10.1016\/j.cpc.2026.110306_bib0038","doi-asserted-by":"crossref","DOI":"10.1063\/5.0203869","article-title":"Super-resolution reconstruction of turbulent flows with a hybrid framework of attention","volume":"36","author":"Zeng","year":"2024","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.cpc.2026.110306_bib0039","doi-asserted-by":"crossref","first-page":"9992","DOI":"10.1109\/ICCV48922.2021.00986","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021","journal-title":"2021 IEEE\/CVF Int. Conf. Comput. Vis."},{"key":"10.1016\/j.cpc.2026.110306_bib0040","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"1833","article-title":"SwinIR: image restoration using swin transformer","author":"Liang","year":"2021"},{"key":"10.1016\/j.cpc.2026.110306_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105026","article-title":"SwinFR: combining swinIR and fast fourier for super-resolution reconstruction of remote sensing images","volume":"159","author":"Zhang","year":"2025","journal-title":"Digit. Signal Process."},{"issue":"9","key":"10.1016\/j.cpc.2026.110306_bib0042","article-title":"A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence","volume":"9","author":"Li","year":"2008","journal-title":"J. Turbul."},{"issue":"11","key":"10.1016\/j.cpc.2026.110306_bib0043","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1063\/1.1693365","article-title":"Spectral calculations of isotropic turbulence: efficient removal of aliasing interactions","volume":"14","author":"Patterson","year":"1971","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.cpc.2026.110306_bib0044","series-title":"Proceedings of the 2007 ACM\/IEEE Conference on Supercomputing","first-page":"1","article-title":"Data exploration of turbulence simulations using a database cluster","author":"Perlman","year":"2007"},{"key":"10.1016\/j.cpc.2026.110306_bib0045","series-title":"Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis","first-page":"1","article-title":"Petascale direct numerical simulation of turbulent channel flow on up to 786k cores","author":"Lee","year":"2013"},{"key":"10.1016\/j.cpc.2026.110306_bib0046","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1017\/S0022112087000892","article-title":"Turbulence statistics in fully developed channel flow at low Reynolds number","volume":"177","author":"Kim","year":"1987","journal-title":"J. Fluid Mech."},{"issue":"6","key":"10.1016\/j.cpc.2026.110306_bib0047","doi-asserted-by":"crossref","DOI":"10.1175\/1520-0469(1971)028<1074:OTEOAI>2.0.CO;2","article-title":"On the elimination of aliasing in finite-difference schemes by filtering high-wavenumber components","volume":"28","author":"Orszag","year":"1971","journal-title":"J. Atmos. Sci."},{"issue":"5","key":"10.1016\/j.cpc.2026.110306_bib0048","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TIP.2004.826093","article-title":"Linear interpolation revitalized","volume":"13","author":"Blu","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.cpc.2026.110306_bib0049","series-title":"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","first-page":"1316","article-title":"Improvements on bicubic image interpolation","volume":"vol. 1","author":"Li","year":"2019"},{"key":"10.1016\/j.cpc.2026.110306_bib0050","unstructured":"H. Gholamalinezhad, H. Khosravi, Pooling methods in deep neural networks, a review, arXiv preprint arXiv: 2009.07485(2020)."},{"key":"10.1016\/j.cpc.2026.110306_bib0051","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.cpc.2026.110306_bib0052","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"Peerj Comput. Sci."},{"issue":"8","key":"10.1016\/j.cpc.2026.110306_bib0053","doi-asserted-by":"crossref","DOI":"10.1063\/5.0160755","article-title":"A swin-transformer-based model for efficient compression of turbulent flow data","volume":"35","author":"Zhang","year":"2023","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.cpc.2026.110306_bib0054","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6246","article-title":"Swift parameter-free attention network for efficient super-resolution","author":"Wan","year":"2024"},{"issue":"4","key":"10.1016\/j.cpc.2026.110306_bib0055","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Computer Physics Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010465526002882?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010465526002882?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T21:11:13Z","timestamp":1785445873000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010465526002882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":55,"alternative-id":["S0010465526002882"],"URL":"https:\/\/doi.org\/10.1016\/j.cpc.2026.110306","relation":{},"ISSN":["0010-4655"],"issn-type":[{"value":"0010-4655","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Turbulent super-resolution reconstruction based on residual convolutional attention mechanism","name":"articletitle","label":"Article Title"},{"value":"Computer Physics Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cpc.2026.110306","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110306"}}