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Vis., Paris, France, Oct. 2\u20136, 2023, pp. 23073\u201323084."},{"key":"ref5","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":"Dec. 1981","journal-title":"IEEE Trans. Acoust., Speech, Signal Process."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"7521","DOI":"10.1007\/s13369-018-3150-1","article-title":"Enhanced iterative back-projection based super-resolution reconstruction of digital images","volume":"43","author":"Nayak","year":"Dec. 2018","journal-title":"Arab. J. Sci. Eng."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1007\/s11801-019-8138-x","article-title":"Single image super-resolution reconstruction using multiple dictionaries and improved iterative back-projection","volume":"15","author":"Zhao","year":"Apr. 2019","journal-title":"Optoelectron. Lett."},{"key":"ref8","unstructured":"B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, \u201cEnhanced deep residual networks for single image super-resolution,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, Honolulu, HI, USA, Jul. 21\u201326, 2017, pp. 136\u2013144."},{"key":"ref9","unstructured":"K. T. Gribbon and D. G. Bailey, \u201cA novel approach to real-time bilinear interpolation,\u201d presented at the 2nd IEEE Int. Workshop Electron. Design, Test Appl., Perth, WA, Australia, Jan. 28\u201330, 2004, pp. 126\u2013131."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"16185","DOI":"10.1007\/s10489-022-03375-w","article-title":"IBPNet: A multi-resolution and multi-modal image fusion network via iterative back-projection","volume":"52","author":"Liu","year":"Dec. 2022","journal-title":"Appl. Intell."},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"B. Xia, Y. Hang, Y. Tian, W. Yang, and J. 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Yang, \u201cDeep laplacian pyramid networks for fast and accurate super-resolution,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 21\u201326, 2017, pp. 624\u2013632."},{"key":"ref15","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TCYB.2019.2952710","article-title":"Cascading and enhanced residual networks for accurate single-image super-resolution","volume":"51","author":"Lan","year":"Jan. 2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"J. Kim, J. K. Lee, and K. M. Lee, \u201cAccurate image super-resolution using very deep convolutional networks,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, Jun. 27\u201330, 2016, pp. 1646\u20131654.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref17","unstructured":"C. Ledig et al., \u201cPhoto-realistic single image super-resolution using a generative adversarial network,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 21\u201326, 2017, pp. 4681\u20134690."},{"key":"ref18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"Feb. 2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, \u201cResidual dense network for image super-resolution,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Salt Lake City, UT, USA, Jun. 18\u201322, 2018, pp. 2472\u20132481.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref20","unstructured":"Y. Guo et al., \u201cClosed-loop matters: Dual regression networks for single image super-resolution,\u201d presented at the IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, Jun. 16\u201318, 2020, pp. 5407\u20135416."},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong and Y. Fu, \u201cImage super-resolution using very deep residual channel attention networks,\u201d presented at the ECCV, Munich, Germany, Sep. 8\u201314, 2018, pp. 286\u2013301.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"B. Niu et al., \u201cSingle image super-resolution via a holistic attention network,\u201d presented at the ECCV, Glasgow, UK, Aug. 23\u201328, 2020, pp. 191\u2013207.","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"ref23","unstructured":"T. Dai, J. Cai, Y. Zhang, S. T. Xia, and L. Zhang, \u201cSecond-order attention network for single image super-resolution,\u201d presented at the IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., Long Beach, CA, USA, Jun. 16\u201320, 2019, pp. 11065\u201311074."},{"key":"ref24","article-title":"Generative adversarial nets","volume":"27","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"X. Wang et al., \u201cEsrgan: Enhanced super-resolution generative adversarial networks,\u201d presented at the ECCV Workshops, Munich, Germany, Sep. 8\u201314, 2018.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref26","unstructured":"K. Zhang, L. V. Gool, and R. Timofte, \u201cDeep unfolding network for image super-resolution presented in IEEE\/CVF Conf,\u201d presented at the IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, Jun. 16\u201318, 2020, pp. 3217\u20133226."},{"key":"ref27","doi-asserted-by":"crossref","first-page":"7898","DOI":"10.1109\/TPAMI.2021.3114428","article-title":"Structure-preserving image super-resolution","volume":"44","author":"Ma","year":"Nov. 2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref28","unstructured":"J. Park, S. Son, and K. M. Lee, \u201cContent-aware local gan for photo-realistic super-resolution,\u201d presented at the IEEE\/CVF Int. Conf. Comput. Vis., Paris, France, Oct. 2\u20136, 2023, pp. 10585\u201310594."},{"key":"ref29","unstructured":"H. Chen et al., \u201cPre-trained image processing transformer,\u201d presented at the IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., Nashville, TN, USA, Jun. 19\u201325, 2021, pp. 12299\u201312310."},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"J. Liang, J. Cao, G. Sun, K. Zhang, and R. Timofte, \u201cSwinIR: Image restoration using swin transformer,\u201d presented at the IEEE\/CVF Int. Conf. Comput. Vis., Montreal, QC, Canada, Oct. 11\u201317, 2021, pp. 1833\u20131844.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref31","unstructured":"M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, \u201cLow-complexity single-image super-resolution based on nonnegative neighbor embedding,\u201d presented at the BMVC, Surrey, UK, Sep. 3\u20137, 2012."},{"key":"ref32","first-page":"711","article-title":"On single image scale-up using sparse-representations","author":"Zeyde","year":"Jun. 24\u201330, 2012","journal-title":"Curves Surfaces"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"J. B. Huang, A. Singh, and N. Ahuja, \u201cSingle image super-resolution from transformed self-exemplars,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, Jun. 7\u201312, 2015, pp. 5197\u20135206.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","article-title":"Sketch-based manga retrieval using manga109 dataset","volume":"76","author":"Matsui","year":"Sep. 2017","journal-title":"Multimed. Tools Appl."},{"key":"ref35","article-title":"Super resolution for root imaging","volume":"8","author":"Ruiz-Munoz","year":"Jul. 2020, Art. no. e11374","journal-title":"Appl. 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