{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:03:55Z","timestamp":1768406635889,"version":"3.49.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"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":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11554-024-01565-y","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T20:44:33Z","timestamp":1729629873000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Partial convolutional reparameterization network for lightweight image super-resolution"],"prefix":"10.1007","volume":"21","author":[{"given":"Long","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yi","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"1565_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"1565_CR2","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., Sohn, K. A.: Fast, accurate, and, lightweight super-resolution with cascading residual network (2018). https:\/\/api.semanticscholar.org\/CorpusID:4710341. arXiv:1803.08664","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"1565_CR3","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Morel, A.: Low-complexity single image super-resolution based on nonnegative neighbor embedding. In: British Machine Vision Conference (2012)","DOI":"10.5244\/C.26.135"},{"key":"1565_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.-H., He, H., Zhuo, W., Wen, S., Lee, C.-H., Gary Chan, S.-H.: Run, don\u2019t walk: chasing higher flops for faster neural networks (2023). arXiv preprint arXiv:2303.03667","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"1565_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., Yu, F.: Dual aggregation transformer for image super-resolution. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"1565_CR6","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1800\u20131807 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"1565_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111156","volume":"282","author":"Y Cui","year":"2023","unstructured":"Cui, Y., Knoll, A.: Exploring the potential of channel interactions for image restoration. Knowl. Based Syst. 282, 111156 (2023)","journal-title":"Knowl. Based Syst."},{"key":"1565_CR8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Image restoration via frequency selection. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3330416"},{"key":"1565_CR9","doi-asserted-by":"crossref","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Focal network for image restoration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pages 13001\u201313011 (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1565_CR10","doi-asserted-by":"crossref","unstructured":"Cui, Y., Ren, W., Yang, S., Cao, X., Knoll, A.: Irnext: rethinking convolutional network design for image restoration. In: Proceedings of the 40th International Conference on Machine Learning (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1565_CR11","doi-asserted-by":"crossref","unstructured":"Cui, Y., Tao, Y., Bing, Z., Ren, W., Gao, X., Cao, X., Huang, K., Knoll, A.: Selective frequency network for image restoration. In: The Eleventh International Conference on Learning Representations (2023)","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"1565_CR12","doi-asserted-by":"publisher","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Revitalizing convolutional network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201316 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3419007","DOI":"10.1109\/TPAMI.2024.3419007"},{"key":"1565_CR13","doi-asserted-by":"publisher","unstructured":"Cui, Y., Ren, W., Knoll, A.: Omni-kernel network for image restoration. Proc. AAAI Conf. Artif. Intell. 38(2), 1426\u20131434 (2024). https:\/\/doi.org\/10.1609\/aaai.v38i2.27907. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/27907","DOI":"10.1609\/aaai.v38i2.27907"},{"key":"1565_CR14","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Ding, G., Han, J.: Acnet: strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00200"},{"key":"1565_CR15","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., Ding, G.: Diverse branch block: Building a convolution as an inception-like unit. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 10886\u201310895 (2021)","DOI":"10.1109\/CVPR46437.2021.01074"},{"key":"1565_CR16","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: Making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1565_CR17","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C. C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Computer Vision \u2013 ECCV 2014. Springer International Publishing, Cham, pp. 184\u2013199 (2014) (ISBN 978-3-319-10593-2)","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"1565_CR18","doi-asserted-by":"crossref","unstructured":"Dong, C., Change Loy C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision (2016). https:\/\/api.semanticscholar.org\/CorpusID:13271756","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"1565_CR19","doi-asserted-by":"crossref","unstructured":"Fang, G., Ma, X., Song, M., Mi, M. B., Wang, X.: Depgraph: towards any structural pruning. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"1565_CR20","first-page":"661","volume":"36","author":"G Gao","year":"2022","unstructured":"Gao, G., Li, W., Li, J., Fei, W., Huimin, L., Yi, Y.: Feature distillation interaction weighting network for lightweight image super-resolution. Proc. AAAI Conf. Artif. Intell. 36, 661\u2013669 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1565_CR21","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In: Bengio, Y., LeCun, Y. (eds) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings (2016)"},{"key":"1565_CR22","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv Learning (2016). https:\/\/api.semanticscholar.org\/CorpusID:125617073"},{"key":"1565_CR23","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:1503.02531"},{"key":"1565_CR24","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017). https:\/\/api.semanticscholar.org\/CorpusID:12670695. arXiv: 1704.04861"},{"key":"1565_CR25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1565_CR26","doi-asserted-by":"crossref","unstructured":"Huang, J. B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: IEEE (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1565_CR27","doi-asserted-by":"publisher","unstructured":"Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 723\u2013731 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00082","DOI":"10.1109\/CVPR.2018.00082"},{"key":"1565_CR28","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th ACM International Conference on Multimedia (ACM MM), pages 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"1565_CR29","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds) Advances in Neural Information Processing Systems, volume\u00a028. Curran Associates, Inc. (2015)"},{"key":"1565_CR30","doi-asserted-by":"publisher","unstructured":"Kim, J., Lee, J. K., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1637\u20131645 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.181","DOI":"10.1109\/CVPR.2016.181"},{"key":"1565_CR31","doi-asserted-by":"publisher","unstructured":"Kim, J., Lee, J. K., Mu Lee K.: Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1646\u20131654 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.182","DOI":"10.1109\/CVPR.2016.182"},{"key":"1565_CR32","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)"},{"key":"1565_CR33","doi-asserted-by":"publisher","unstructured":"Kong, F., Li, M., Liu, S., Liu, D., He, J., Bai, Y., Chen, F., Fu, L.: Residual local feature network for efficient super-resolution. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 765\u2013775 (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00092","DOI":"10.1109\/CVPRW56347.2022.00092"},{"key":"1565_CR34","doi-asserted-by":"publisher","unstructured":"Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5835\u20135843 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.618","DOI":"10.1109\/CVPR.2017.618"},{"key":"1565_CR35","doi-asserted-by":"publisher","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 105\u2013114 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.19","DOI":"10.1109\/CVPR.2017.19"},{"key":"1565_CR36","doi-asserted-by":"crossref","unstructured":"Lee, W., Lee, J., Kim, D., Ham, B.: Learning with privileged information for efficient image super-resolution. In: Proceedings of European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58586-0_28"},{"key":"1565_CR37","doi-asserted-by":"publisher","unstructured":"Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., Chen, Y.: Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing, 479: 47\u201359 (2022). (ISSN 0925-2312). https:\/\/doi.org\/10.1016\/j.neucom.2022.01.029","DOI":"10.1016\/j.neucom.2022.01.029"},{"key":"1565_CR38","unstructured":"Li, W., Zhou, K., Qi, L., Jiang, N., Lu, J., Jia, J.: Lapar: linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds) Advances in Neural Information Processing Systems, volume\u00a033, pages 20343\u201320355. Curran Associates Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/eaae339c4d89fc102edd9dbdb6a28915-Paper.pdf"},{"key":"1565_CR39","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, Y., Chen, X., Cai, H., Gu, J., Qiao, Y., Dong, C.: Blueprint separable residual network for efficient image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 833\u2013843 (2022)","DOI":"10.1109\/CVPRW56347.2022.00099"},{"key":"1565_CR40","doi-asserted-by":"publisher","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pages 1833\u20131844 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00210","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"1565_CR41","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K. M.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1565_CR42","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1565_CR43","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: Bartoli, A., Fusiello, A. (eds) Computer Vision \u2013 ECCV 2020 Workshops, pages 41\u201355. Springer International Publishing, Cham (2020). (ISBN 978-3-030-67070-2)","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"1565_CR44","doi-asserted-by":"publisher","unstructured":"Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G.: Residual feature aggregation network for image super-resolution. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2356\u20132365 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00243","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"1565_CR45","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dong, H., Liang, B., Liu, S., Dong, Q., Chen, K., Chen, F., Fu, L., Wang, F.: Unfolding once is enough: A deployment-friendly transformer unit for super-resolution. In: Proceedings of the 31st ACM International Conference on Multimedia, pages 7952\u20137960 (2023)","DOI":"10.1145\/3581783.3612128"},{"key":"1565_CR46","doi-asserted-by":"publisher","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: 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 2001, volume\u00a02, pages 416\u2013423 (2001). https:\/\/doi.org\/10.1109\/ICCV.2001.937655","DOI":"10.1109\/ICCV.2001.937655"},{"key":"1565_CR47","doi-asserted-by":"crossref","unstructured":"Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. Multimed. Tools Appl. 76: 21811\u201321838 (2015). https:\/\/api.semanticscholar.org\/CorpusID:8887614","DOI":"10.1007\/s11042-016-4020-z"},{"issue":"4","key":"1565_CR48","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1109\/TPAMI.2022.3204461","volume":"45","author":"C Saharia","year":"2023","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, Mohammad: Image super-resolution via iterative refinement. IEEE Transa. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3204461","journal-title":"IEEE Transa. Pattern Anal. Mach. Intell."},{"key":"1565_CR49","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1874\u20131883 (2016). https:\/\/api.semanticscholar.org\/CorpusID:7037846","DOI":"10.1109\/CVPR.2016.207"},{"key":"1565_CR50","doi-asserted-by":"crossref","unstructured":"Sun, L., Dong, J., Tang, J., Pan, J.: Spatially-adaptive feature modulation for efficient image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"1565_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning (2016). ArXiv:1602.07261. https:\/\/api.semanticscholar.org\/CorpusID:1023605","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1565_CR52","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818\u20132826 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"1565_CR53","doi-asserted-by":"publisher","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2790\u20132798 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.298","DOI":"10.1109\/CVPR.2017.298"},{"key":"1565_CR54","doi-asserted-by":"publisher","unstructured":"Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: 2017 IEEE International Conference on Computer Vision (ICCV), pages 4809\u20134817 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.514","DOI":"10.1109\/ICCV.2017.514"},{"key":"1565_CR55","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: Revisiting mobile cnn from vit perspective, Repvit (2023)","DOI":"10.1109\/CVPR52733.2024.01506"},{"key":"1565_CR56","unstructured":"Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: Towards real-time segmenting anything, Repvit-sam (2023)"},{"key":"1565_CR57","doi-asserted-by":"crossref","unstructured":"Wang, L., Dong, X., Wang, Y., Ying, X., Lin, Z., An, W., Guo, Y.: Exploring sparsity in image super-resolution for efficient inference. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00488"},{"key":"1565_CR58","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11531\u201311539 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1565_CR59","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change Loy, C.: Esrgan: enhanced super-resolution generative adversarial networks. In: The European Conference on Computer Vision Workshops (ECCVW) (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"issue":"4","key":"1565_CR60","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"1565_CR61","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon Cbam, I. S.: Convolutional block attention module. In: Computer Vision \u2013 ECCV 2018. Springer International Publishing, Cham, pages 3\u201319 (2018). (ISBN 978-3-030-01234-2)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1565_CR62","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: On single image scale-up using sparse-representations. In: International Conference on Curves and Surfaces (2010)"},{"key":"1565_CR63","unstructured":"Yulun, Z., Kunpeng, L., Kai, L., Lichen, W., Bineng, Z., Yun, F.: Image super-resolution using very deep residual channel attention networks. In: ECCV (2018)"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01565-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01565-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01565-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:16:43Z","timestamp":1732799803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01565-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1565"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01565-y","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"14 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"187"}}