{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:20:56Z","timestamp":1774315256920,"version":"3.50.1"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61976098"],"award-info":[{"award-number":["61976098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["1976098"],"award-info":[{"award-number":["1976098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the National Key R &D Program of China","award":["2021YFE0205400"],"award-info":[{"award-number":["2021YFE0205400"]}]},{"name":"the Collaborative Innovation Platform Project of Fujian Province","award":["2021FX03"],"award-info":[{"award-number":["2021FX03"]}]},{"name":"the Natural Science Foundation of Fujian Provincial Science and Technology Department","award":["2021H6037"],"award-info":[{"award-number":["2021H6037"]}]},{"name":"the Natural Science Foundation of Fujian Province","award":["2019J01010561"],"award-info":[{"award-number":["2019J01010561"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["ZQN-921"],"award-info":[{"award-number":["ZQN-921"]}]},{"name":"the Foundation of Fujian Education Department","award":["JAT170053"],"award-info":[{"award-number":["JAT170053"]}]},{"name":"the Key Project of Quanzhou Science and Technology Plan","award":["2021C008R"],"award-info":[{"award-number":["2021C008R"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00371-023-03200-6","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T06:01:52Z","timestamp":1703052112000},"page":"7667-7684","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dtsr: detail-enhanced transformer for image super-resolution"],"prefix":"10.1007","volume":"40","author":[{"given":"Xiaoqian","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-3728","authenticated-orcid":false,"given":"Detian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caixia","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiyang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengjun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"3200_CR1","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Ledig, C., Zhuang, X., Bai, W., Bhatia, K., de Marvao, A.M.S.M., Dawes, T., ORegan, D., Rueckert, D.: Cardiac image super-resolution with global correspondence using multi-atlas PatchMatch. In: MICCAI (2013)","DOI":"10.1007\/978-3-642-40760-4_2"},{"key":"3200_CR2","doi-asserted-by":"crossref","unstructured":"Zou, W.W., Yuen, P.C.: Very low-resolution face recognition problem. In: TIP (2012)","DOI":"10.1109\/TIP.2011.2162423"},{"key":"3200_CR3","doi-asserted-by":"publisher","first-page":"17760","DOI":"10.1109\/ACCESS.2022.3147493","volume":"10","author":"Z Gao","year":"2022","unstructured":"Gao, Z., Chen, J.: Maritime infrared image super-resolution using cascaded residual network and novel evaluation metric. IEEE Access 10, 17760\u201317767 (2022)","journal-title":"IEEE Access"},{"key":"3200_CR4","unstructured":"Chen, B., Gu, S.: Research on super-resolution reconstruction of meteorological satellite remote sensing images. In: Proceedings of the 28th Annual Meeting of the Chinese Meteorological Society, pp. 1\u20139. Chinese Meteorological Society, Beijing (2011)"},{"key":"3200_CR5","doi-asserted-by":"crossref","unstructured":"ong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184\u2013199 (2014)","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"3200_CR6","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"issue":"7","key":"3200_CR7","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.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"key":"3200_CR9","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., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"3200_CR10","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Kyoung Mu Lee.: Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"3200_CR11","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: ESRGAN: enhanced super-resolution generative adversarial networks. In: European Conference on Computer Vision Workshops, pp. 701\u2013710 (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"issue":"9","key":"3200_CR12","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: European Conference on Computer Vision, pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"3200_CR14","doi-asserted-by":"crossref","unstructured":"Fritsche, M., Gu, S., Timofte, R.: Frequency Separation for Real-World Super-Resolution. In: IEEE Conference on International Conference on Computer Vision Workshops, pp. 3599\u20133608 (2019)","DOI":"10.1109\/ICCVW.2019.00445"},{"key":"3200_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262\u20133271 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"3200_CR16","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., Wu, W.: Feedback network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3867\u20133876 (2019)","DOI":"10.1109\/CVPR.2019.00399"},{"key":"3200_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: IEEE Conference on International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"3200_CR18","unstructured":"Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1\u20138 (2021)"},{"key":"3200_CR19","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., Gool, L.V.: Adjusted anchored neighborhood regression for fast superresolution. In Asian Conference on Computer Vision, vol. 1, No. 2, pp. 111\u2013126 (2014)","DOI":"10.1007\/978-3-319-16817-3_8"},{"issue":"8","key":"3200_CR20","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR21","unstructured":"Gu, S., Sang, N., Ma, F.: Fast image super resolution via local regression. In: IEEE Conference on International Conference on Pattern Recognition, pp. 3128\u20133131 (2012)"},{"key":"3200_CR22","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3200_CR23","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations (ICLR), pp. 1\u20139 (2021)"},{"key":"3200_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. arXiv:2103.14030 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3200_CR25","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"3200_CR26","unstructured":"Li, W., Lu, X., Lu, J., Zhang, X., Jia, J.: On efficient transformer and image pre-training for low-level vision. arXiv:2112.10175 (2021)"},{"key":"3200_CR27","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: Image restoration using Swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW) (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"3200_CR28","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"3200_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. arXiv:2203.06697 (2022)","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"3200_CR30","doi-asserted-by":"crossref","unstructured":"Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9199\u20139208 (2021)","DOI":"10.1109\/CVPR46437.2021.00908"},{"key":"3200_CR31","doi-asserted-by":"crossref","unstructured":"Anoosheh, A., Sattler, T., Timofte, R., Pollefeys, M., Van Gool, L.: Night-to-day image translation for retrieval-based localization. In: ICRA, pp. 5958\u20135964 (2019)","DOI":"10.1109\/ICRA.2019.8794387"},{"key":"3200_CR32","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J. Zhou, J.: Structure-preserving super resolution with gradient guidance. arXiv:2003.13081 (2020)","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"3200_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, Y., Bonev, B., Yuille, A.L.: Modeling deformable gradient compositions for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7299180"},{"issue":"12","key":"3200_CR34","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1109\/TIP.2017.2750403","volume":"26","author":"W Yang","year":"2017","unstructured":"Yang, W., Feng, J., Yang, J., Zhao, F., Liu, J., Guo, Z., Yan, S.: Deep edge guided recurrent residual learning for image superresolution. IEEE Trans. Image Process. 26(12), 5895\u20135907 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"3200_CR35","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","volume":"57","author":"K Jiang","year":"2019","unstructured":"Jiang, K., Wang, Z., Yi, P., Wang, G., Lu, T., Jiang, J.: Edge-enhanced GAN for remote sensing image superresolution. IEEE Trans. Geosci. Remote Sens. 57(8), 5799\u20135812 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2902431","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3200_CR36","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"3200_CR37","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for superresolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1664\u20131673 (2018)","DOI":"10.1109\/CVPR.2018.00179"},{"key":"3200_CR38","unstructured":"Mei, Y., Fan, Y., Zhang, Y., Yu, J., Zhou, Y., Liu, D., Fu, Y., Huang, T.S., Shi, H.: Pyramid Attention Networks for Image Restoration. arXiv:2004.13824 (2020)"},{"key":"3200_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"3200_CR40","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., Tan, M.: Closed-loop matters: dual regression networks for single image super-resolution. arXiv:2003.07018 (2020)","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"3200_CR41","doi-asserted-by":"publisher","unstructured":"Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., Shi, H.: Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining, In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5689\u20135698. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00573(2020)","DOI":"10.1109\/CVPR42600.2020.00573"},{"key":"3200_CR42","unstructured":"Zhou, S., Zhang, J., Zuo, W., Loy, C.C.: Cross-scale internal graph neural network for image super-resolution. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3499\u20133509 (2020)"},{"key":"3200_CR43","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20132 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"3200_CR44","doi-asserted-by":"crossref","unstructured":"Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., Shen, H.: Single image super-resolution via a holistic attention network. In: European Conference on Computer Vision, pp. 191\u2013207. Springer (2020)","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"3200_CR45","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3517\u20133526 (2021)","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"3200_CR46","first-page":"1","volume-title":"Fractals Everywhere","author":"MF Barnsley","year":"1998","unstructured":"Barnsley, M.F.: Fractals Everywhere, pp. 1\u20132. Academic Press, New York (1998)"},{"key":"3200_CR47","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)"},{"key":"3200_CR48","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"3200_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Liu, J.: Uformer: A General U-Shaped Transformer for Image Restoration. arXiv:2106.03106 (2021)","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"3200_CR50","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. arXiv:2111.09881 (2021)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"3200_CR51","unstructured":"Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. arXiv:2201.12288 (2022)"},{"key":"3200_CR52","unstructured":"Lin, Z., Garg, P., Banerjee, A., Magid, S.A., Sun, D., Zhang, Y., Van Gool, L., Wei, D., Pfister, H.: Revisiting rcan: improved training for image super-resolution. arXiv:2201.11279 (2022)"},{"issue":"3","key":"3200_CR53","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1145\/1276377.1276496","volume":"26","author":"R Fattal","year":"2007","unstructured":"Fattal, R.: Image upsampling via imposed edge statistics. ACM Trans. Graph. (TOG) 26(3), 95 (2007)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"6","key":"3200_CR54","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TIP.2010.2095871","volume":"20","author":"J Sun","year":"2010","unstructured":"Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. (TIP) 20(6), 1529\u20131542 (2010)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"issue":"10","key":"3200_CR55","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1109\/TIP.2015.2414877","volume":"24","author":"Q Yan","year":"2015","unstructured":"Yan, Q., Xu, Y., Yang, X., Nguyen, T.Q.: Single image superresolution based on gradient profile sharpness. IEEE Trans. Image Process. 24(10), 3187\u20133202 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR56","doi-asserted-by":"crossref","unstructured":"Li, K., Wang, Y., Zhang, J., Gao, P., Song, G., Liu, Y., Li, H., Qiao, Y.: Uniformer: unifying convolution and self-attention for visual recognition. arXiv:2201.09450 (2022)","DOI":"10.1109\/TPAMI.2023.3282631"},{"key":"3200_CR57","unstructured":"Xiao, T., Dollar, P., Singh, M., Mintun, E., Darrell, T., Girshick, R.: Early convolutions help transformers see better. In: Advances in Neural Information Processing Systems, vol. 3 (2021)"},{"key":"3200_CR58","unstructured":"Lin, H., RoyChowdhury, A., Maji, S.: CAT: Cross attention in vision transformer. arXiv:2106.05786 (2021)"},{"issue":"1","key":"3200_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2022.3152247","volume":"44","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, X., Xu, Y., Yang, Z., Zhang, Y., Tao, D.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 1\u201314 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3152247","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3200_CR60","unstructured":"Cao, J., Li, Y., Zhang, K., Gool, L.V. Video Super-Resolution Transformer. arXiv:2106.06847 (2021)"},{"key":"3200_CR61","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"3200_CR62","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-Complexity Single-Image Super-Resolution Based on Nonnegative Neighbor Embedding, pp. 8, 13, 16 (2012)"},{"key":"3200_CR63","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse representations. In: International Conference on Curves and Surfaces, pp. 711\u2013730. Springer (2010)","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"3200_CR64","doi-asserted-by":"crossref","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 (Vol. 2), pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"3200_CR65","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N. Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"20","key":"3200_CR66","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.: Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appl. 76(20), 21811\u201321838 (2017)","journal-title":"Multimedia Tools Appl."},{"key":"3200_CR67","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"3200_CR68","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319\u20133328 (2017)"},{"key":"3200_CR69","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the International Conference on Machine Learning, pp. 3145\u20133153 (2017)"},{"key":"3200_CR70","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034 (2013)"},{"key":"3200_CR71","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. arXiv:1412.6806 (2014)"},{"key":"3200_CR72","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768\u20134777 (2017)"},{"issue":"8","key":"3200_CR73","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR74","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862\u20132869 (2014)","DOI":"10.1109\/CVPR.2014.366"},{"issue":"7","key":"3200_CR75","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.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"3200_CR77","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"3200_CR78","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"3200_CR79","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: ACM International Conference on Multimedia, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"3200_CR80","doi-asserted-by":"crossref","unstructured":"Isobe, T., Jia, X., Gu, S., Li, S., Wang, S., Tian, Q.: Video super-resolution with recurrent structure-detail network. In: European Conference on Computer Vision, pp. 645\u2013660 (2020)","DOI":"10.1007\/978-3-030-58610-2_38"},{"key":"3200_CR81","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 466\u2013467 (2020)","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"3200_CR82","doi-asserted-by":"crossref","unstructured":"Jia, X., Liu, S., Feng, X., Zhang, L.: FocNet: A fractional optimal control network for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6054\u20136063 (2019)","DOI":"10.1109\/CVPR.2019.00621"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03200-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03200-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03200-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:11:01Z","timestamp":1730909461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03200-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"references-count":82,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3200"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03200-6","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,20]]},"assertion":[{"value":"12 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}