{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:21:06Z","timestamp":1775848866832,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21172-2","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:10:56Z","timestamp":1775844656000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CSRD: concurrent super-resolution and denoising via data fidelity and prior terms"],"prefix":"10.1007","volume":"85","author":[{"given":"Saghar","family":"Farhangfar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aryaz","family":"Baradarani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Asadpour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Ali","family":"Balafar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman Gr.","family":"Maev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"21172_CR1","volume-title":"Machine learning: a probabilistic perspective","author":"KP Murphy","year":"2013","unstructured":"Murphy KP (2013) Machine learning: a probabilistic perspective. MIT Press, Cambridge, Massachusetts"},{"key":"21172_CR2","doi-asserted-by":"crossref","unstructured":"Farhangfar S, Baradarani A, Balafar MA, Asadpour M (2022) SSTRN: semantic style transfer reference network for face super-resolution. in: IEEE 29th International Conference on Systems, Signals and Image Processing (IWSSIP), pp 2\u20134","DOI":"10.1109\/IWSSIP55020.2022.9854432"},{"key":"21172_CR3","doi-asserted-by":"crossref","unstructured":"Baradarani A, Shapoori K, Farhangfar S, Sadler J, Malyarenko E, Gelovani JG, Maev RGr (2023) Superresolution with embedded denoising via image frequency separation and convolutional neural network in a prototyped transcranial ultrasound brain imaging scanner. In: IEEE International Ultrasonics Symposium (IUS), pp 1\u20134","DOI":"10.1109\/IUS51837.2023.10306624"},{"key":"21172_CR4","unstructured":"Thomas E, Jian S, Jean P (2020) End-to-end interpretable learning of non-blind image deblurring. In: European Conference on Computer Vision (ECCV), pp 23\u201328"},{"key":"21172_CR5","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision (ECCV), pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"21172_CR6","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image superresolution. In: IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"21172_CR7","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"21172_CR8","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Loy CC (2019) ESRGAN: enhanced superresolution generative adversarial networks. In: European Conference on Computer Vision (ECCV), pp 63\u201379","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"21172_CR9","doi-asserted-by":"crossref","unstructured":"Korkmaz C, Tekalp AM, Dogan Z (2024) Training generative image super-resolution models by waveletdomain losses enables better control of artifacts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5926\u20135936","DOI":"10.1109\/CVPR52733.2024.00566"},{"key":"21172_CR10","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TMM.2020.2999182","volume":"23","author":"C Tian","year":"2021","unstructured":"Tian C, Xu Y, Zuo W, Zhang B, Fei L, Lin C-W (2021) Coarse-to-fine CNN for image super-resolution. IEEE Trans Multimedia 23:1489\u20131502","journal-title":"IEEE Trans Multimedia"},{"key":"21172_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: European Conference on Computer Vision (ECCV), pp 294\u2013310","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"21172_CR12","doi-asserted-by":"crossref","unstructured":"Liang J, Cao J, Sun G, Zhang K, Van Gool L, Timofte R (2021) SwinIR: image restoration using Swin transformer. In: IEEE International Conf on Computer Vision (ICCV), pp 1833\u20131844","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"21172_CR13","doi-asserted-by":"crossref","unstructured":"Chen X, Wang X, Zhou J, Qiao Y, Dong C (2023) Activating more pixels in image super-resolution transformer. In: IEEE Conf Computer Vision and Pattern Recognition (CVPR), pp 22367\u201322377","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"21172_CR14","doi-asserted-by":"crossref","unstructured":"Conde MV, Choi U-J, Burchi M, Timofte R (2022) Swin2SR: swinv2 transformer for compressed image super-resolution and restoration. In: European Conference on Computer Vision (ECCV), pp 669\u2013687","DOI":"10.1007\/978-3-031-25063-7_42"},{"key":"21172_CR15","doi-asserted-by":"crossref","unstructured":"Li B, Li X, Lu Y, Liu S, Feng R, Chen Z (2023) HST: hierarchical Swin transformer for compressed image super-resolution. In: European Conf on Computer Vision (ECCV), pp 651\u2013668","DOI":"10.1007\/978-3-031-25063-7_41"},{"key":"21172_CR16","doi-asserted-by":"crossref","unstructured":"Gao G, Wang Z, Li J, Li W, Yu Y, Zeng T (2022) Lightweight bimodal network for single-image super-resolution via symmetric CNN and recursive transformer. In: 31st International Joint Conference on Artificial Intelligence (IJCAI), pp 913\u2013919","DOI":"10.24963\/ijcai.2022\/128"},{"key":"21172_CR17","doi-asserted-by":"crossref","unstructured":"Sun L, Dong J, Tang J, Pan J (2023) Spatially-adaptive feature modulation for efficient image superresolution. In: IEEE International Conference on Computer Vision (ICCV), pp 13144\u201313153","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"21172_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2022.01.029","volume":"479","author":"H Li","year":"2022","unstructured":"Li H, Yang Y, Chang M, Chen S, Feng H, Xu Z, Li Q, Chen Y (2022) SRDiff: single image superresolution with diffusion probabilistic models. Neurocomputing 479:47\u201359","journal-title":"Neurocomputing"},{"key":"21172_CR19","doi-asserted-by":"crossref","unstructured":"Xia B, Zhang Y, Wang S, Wang Y, Wu X, Tian Y, Yang W, Van Gool L (2023) DiffIR: efficient diffusion model for image restoration. In: IEEE International Conf on Computer Vision (ICCV), pp 13049\u201313059","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"21172_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111995","volume":"299","author":"L Xu","year":"2024","unstructured":"Xu L, Zhou H, Chen Q, Li G (2024) Generation diffusion degradation: simple and efficient design for blind super-resolution. Knowl-Based Syst 299:111995","journal-title":"Knowl-Based Syst"},{"key":"21172_CR21","doi-asserted-by":"crossref","unstructured":"Wang X, Xie L, Dong C, Shan Y (2021) Real-ESRGAN: training real-world blind super-resolution with pure synthetic data. In: IEEE Intl Conf on Computer Vision (ICCV), pp 1905\u20131914","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"21172_CR22","doi-asserted-by":"crossref","unstructured":"Umer RM, Foresti GL, Micheloni C (2020) Deep generative adversarial residual convolutional networks for real-world super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1769\u20131777","DOI":"10.1007\/978-3-030-67070-2_29"},{"issue":"7","key":"21172_CR23","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142\u20133155","journal-title":"IEEE Trans Image Process"},{"key":"21172_CR24","doi-asserted-by":"publisher","unstructured":"Farhangfar S, Baradarani A, Asadpour M, Balafar MA, Maev RGr (2024) Simultaneous single image super-resolution and blind Gaussian denoising via slim ghost full-frequency residual blocks. IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.13230","DOI":"10.1049\/ipr2.13230"},{"key":"21172_CR25","doi-asserted-by":"crossref","unstructured":"Umer RM, Foresti G, Micheloni C (2021) Deep iterative residual convolutional network for single image super-resolution. In: International Conference on Pattern Recognition (ICPR), pp 10\u201311","DOI":"10.1109\/ICPR48806.2021.9412159"},{"issue":"7","key":"21172_CR26","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/83.392335","volume":"4","author":"D Geman","year":"1995","unstructured":"Geman D, Yang C (1995) Nonlinear image recovery with half-quadratic regularization. IEEE Trans on Image Processing 4(7):932\u2013946","journal-title":"IEEE Trans on Image Processing"},{"key":"21172_CR27","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"21172_CR28","doi-asserted-by":"crossref","unstructured":"Zhnag K, Van Gool L, Timofte R (2020) Deep unfolding network for image super-resolution. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp 16\u201318","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"21172_CR29","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"21172_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.103007","volume":"199","author":"Z Wen","year":"2020","unstructured":"Wen Z, Guan J, Zeng T, Li Y (2020) Residual network with detail perception loss for single image super-resolution. Comput Vis Image Underst 199:103007","journal-title":"Comput Vis Image Underst"},{"key":"21172_CR31","doi-asserted-by":"crossref","unstructured":"Farhangfar S, Baradarani A, Asadpour M, Balafar MA, Maev RGr (2023) Single image super-resolution network with enhanced octave convolution for separating image frequencies. In: IEEE International Conf on Computing Communication and Networking Technologies (ICCCNT), pp 1\u20136","DOI":"10.1109\/ICCCNT56998.2023.10306723"},{"key":"21172_CR32","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wei D, Qin C, Wang H, Pfister H, Fu Y (2021) Context reasoning attention network for image super-resolution. In: IEEE International Conference on Computer Vision (ICCV), pp 11\u201317","DOI":"10.1109\/ICCV48922.2021.00424"},{"key":"21172_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.neunet.2022.06.009","volume":"153","author":"C Tian","year":"2022","unstructured":"Tian C, Yuan Y, Zhang S, Lin C-W, Zuo W, Zhang D (2022) Image super-resolution with an enhanced group convolutional neural network. Neural Netw 153:373\u2013385","journal-title":"Neural Netw"},{"key":"21172_CR34","doi-asserted-by":"publisher","first-page":"6507","DOI":"10.1109\/TNNLS.2022.3210433","volume":"35","author":"C Tian","year":"2024","unstructured":"Tian C, Zhang Y, Zuo W, Lin C-W, Zhang D, Yuan Y (2024) A heterogeneous group CNN for image super-resolution. IEEE Trans Neural Netw Learn Syst 35:6507\u20136519","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"21172_CR35","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE International Conference on Computer Vision (ICCV), pp 9992\u201310002","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"21172_CR36","doi-asserted-by":"crossref","unstructured":"Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L, Wei F, Guo B (2022) Swin transformer v2: scaling up capacity and resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 11999\u201312009","DOI":"10.1109\/CVPR52688.2022.01170"},{"issue":"16","key":"21172_CR37","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1109\/TMI.2023.3240862","volume":"42","author":"Z Gao","year":"2023","unstructured":"Gao Z, Guo Y, Zhang J, Zeng T, Yang G (2023) Hierarchical perception adversarial learning framework for compressed sensing MRI. IEEE Trans on Medical Imaging 42(16):1859\u20131874","journal-title":"IEEE Trans on Medical Imaging"},{"key":"21172_CR38","doi-asserted-by":"crossref","unstructured":"Almasri F, Debeir O (2018) Multimodal sensor fusion in single thermal image super-resolution. In: Asian Conference on Computer Vision (ACCV), pp 418\u2013433","DOI":"10.1007\/978-3-030-21074-8_34"},{"issue":"1","key":"21172_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"key":"21172_CR40","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Zhang L (2019) Deep plug-and-play super-resolution for arbitrary blur kernels. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 15\u201320","DOI":"10.1109\/CVPR.2019.00177"},{"key":"21172_CR41","doi-asserted-by":"crossref","unstructured":"Zhang J, Pan J, Lai W-S, Lau RWH, Yang M-H (2017) Learning fully convolutional networks for iterative non-blind deconvolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 21\u201326","DOI":"10.1109\/CVPR.2017.737"},{"issue":"1","key":"21172_CR42","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1198\/0003130042836","volume":"58","author":"DR Hunter","year":"2004","unstructured":"Hunter DR, Lange KL (2004) A tutorial on MM algorithms. Am Stat 58(1):30\u201337","journal-title":"Am Stat"},{"key":"21172_CR43","doi-asserted-by":"crossref","unstructured":"Mou C, Wang Q, Zhang J (2022) Deep generalized unfolding networks for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 19\u201324","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"21172_CR44","unstructured":"Liu Y, Jia Q, Zhang J, Fan X, Wang S, Ma S, Gao W (2022) Hierarchical similarity learning for aliasing suppression image super-resolution. IEEE Trans Neural Netw Learn Syst, 1\u201313"},{"key":"21172_CR45","doi-asserted-by":"crossref","unstructured":"Kelley CT (1995) Iterative methods for linear and nonlinear equations. Soc Ind Appl Math","DOI":"10.1137\/1.9781611970944"},{"key":"21172_CR46","first-page":"4479","volume":"33","author":"L Chi","year":"2020","unstructured":"Chi L, Jiang B, Mu Y (2020) Fast fourier convolution. Adv Neural Inf Process Syst 33:4479\u20134488","journal-title":"Adv Neural Inf Process Syst"},{"key":"21172_CR47","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) GhostNet: more features from cheap operations. In: IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp 1577\u20131586","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"21172_CR48","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 27\u201330","DOI":"10.1109\/CVPR.2016.207"},{"key":"21172_CR49","unstructured":"Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR)"},{"key":"21172_CR50","unstructured":"Paszke A et al (2019) PyTorch: an imperative style, high-performance deep learning library. In: Proceedings of the 33rd international conference on neural information processing systems, pp 8026\u20138037"},{"issue":"1","key":"21172_CR51","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang Z, Bovik AC (2009) Mean squared error: love it or leave it. A new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98\u2013117","journal-title":"IEEE Signal Process Mag"},{"key":"21172_CR52","doi-asserted-by":"crossref","unstructured":"Agustsson E, Timofte R (2017) NTIRE 2017 challenge on single image super-resolution: dataset and study. In: IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp 1122\u20131131","DOI":"10.1109\/CVPRW.2017.150"},{"key":"21172_CR53","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision (ICCV), pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"21172_CR54","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of British Machine Vision Conference (BMVC), pp 1\u201310","DOI":"10.5244\/C.26.135"},{"key":"21172_CR55","doi-asserted-by":"crossref","unstructured":"Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. In: Curves and surfaces, pp 711\u2013730","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"21172_CR56","doi-asserted-by":"crossref","unstructured":"Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"21172_CR57","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui Y, Ito K, Aramaki Y, Fujimoto A, Ogawa T, Yamasaki T, Aizawa K (2017) Sketch-based manga retrieval using manga109 dataset. Multimed Tools Appl 76:21811\u201321838","journal-title":"Multimed Tools Appl"},{"key":"21172_CR58","unstructured":"Franzen R (1999) Kodak lossless true color image suite. http:\/\/r0k.us\/graphics\/kodak. Accessed Jan 2023"},{"issue":"2","key":"21172_CR59","doi-asserted-by":"publisher","DOI":"10.1117\/1.3600632","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang L, Wu X, Buades A, Li X (2011) Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J Electron Imaging 20(2):023016","journal-title":"J Electron Imaging"},{"key":"21172_CR60","doi-asserted-by":"crossref","unstructured":"Abdelhamed A, Lin S, Brown MS (2018) A high-quality denoising dataset for smartphone cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1692\u20131700","DOI":"10.1109\/CVPR.2018.00182"},{"issue":"4","key":"21172_CR61","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"21172_CR62","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp 18\u201323","DOI":"10.1109\/CVPR.2018.00068"},{"key":"21172_CR63","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural betworks. In: NIPS\u201912: Proceedings of the 26th international conference on neural information processing systems, pp 1097\u20131105 (2012)"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21172-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21172-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21172-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:11:14Z","timestamp":1775844674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21172-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":63,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["21172"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21172-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"assertion":[{"value":"13 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"345"}}