{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:05:38Z","timestamp":1772643938549,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04288-8","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T14:37:50Z","timestamp":1765636670000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Kernel-aware dual-domain adaptive network: enhancing blind super-resolution performance"],"prefix":"10.1007","volume":"42","author":[{"given":"Keying","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaping","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zibo","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"4288_CR1","doi-asserted-by":"crossref","unstructured":"Chen, J., Pan, J., Dong, J.: FaithDiff: Unleashing diffusion priors for faithful image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 28188\u201328197 (2025)","DOI":"10.1109\/CVPR52734.2025.02625"},{"key":"4288_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00371-022-02764-z","volume":"40","author":"X Lu","year":"2024","unstructured":"Lu, X., Xie, X., Ye, C., Xing, H., Liu, Z., Cai, C.: A lightweight generative adversarial network for single image super-resolution. Vis. Comput. 40, 41\u201352 (2024)","journal-title":"Vis. Comput."},{"key":"4288_CR3","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s00371-023-02768-3","volume":"40","author":"S Wang","year":"2024","unstructured":"Wang, S., Sun, Z., Li, Q.: High-to-low-level feature matching and complementary information fusion for reference-based image super-resolution. Vis. Comput. 40, 99\u2013108 (2024). https:\/\/doi.org\/10.1007\/s00371-023-02768-3","journal-title":"Vis. Comput."},{"issue":"6","key":"4288_CR4","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.vrih.2023.06.006","volume":"5","author":"Z Li","year":"2023","unstructured":"Li, Z., Pang, X., Jiang, Y., Wang, Y.: Realfuvsr: feature enhanced real-world video sup-er-resolution. Virtual Reality & Intelligent Hardware 5(6), 523\u2013537 (2023). https:\/\/doi.org\/10.1016\/j.vrih.2023.06.006","journal-title":"Virtual Reality & Intelligent Hardware"},{"key":"4288_CR5","doi-asserted-by":"publisher","first-page":"6821","DOI":"10.1109\/TMM.2022.3214776","volume":"25","author":"J Zhu","year":"2023","unstructured":"Zhu, J., Zhang, Q., Fei, L., Cai, R., Xie, Y., Sheng, B., Yang, X.: Fffn: frame-by-frame feedback fusion network for video super-resolution. IEEE Trans. Multimedia 25, 6821\u20136835 (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3214776","journal-title":"IEEE Trans. Multimedia"},{"key":"4288_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/cav.2287","author":"J Ye","year":"2024","unstructured":"Ye, J., Meng, X., Guo, D., Shang, C., Mao, H., Yang, X.: Neural foveated super-resolution for real-time VR rendering. Comput. Anim. Virtual Worlds (2024). https:\/\/doi.org\/10.1002\/cav.2287","journal-title":"Comput. Anim. Virtual Worlds"},{"key":"4288_CR7","doi-asserted-by":"publisher","unstructured":"Gao, G., Wang, Z., Li, J., Li, W., Yu, Y., Zeng, T.: Lightweight bimodal network for single-image super-resolution via symmetric CNN and recursive transformer. In: Proceedings of the international joint conference on artificial intelligence (IJCAI) (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/128","DOI":"10.24963\/ijcai.2022\/128"},{"key":"4288_CR8","doi-asserted-by":"publisher","unstructured":"Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., Zhang, L.: Seesr: Towards semantics-aware real-world image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 25456\u201325467 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02405","DOI":"10.1109\/CVPR52733.2024.02405"},{"key":"4288_CR9","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., Wu, W.: Feedback network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3862\u20133871 (2019)","DOI":"10.1109\/CVPR.2019.00399"},{"key":"4288_CR10","doi-asserted-by":"publisher","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: Proceedings of the European conference on computer vision, pp. 191\u2013207. Springer, (2020) https:\/\/doi.org\/10.1007\/978-3-030-58610-2_12","DOI":"10.1007\/978-3-030-58610-2_12"},{"issue":"4","key":"4288_CR11","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1109\/TMM.2019.2938340","volume":"22","author":"Z Jin","year":"2020","unstructured":"Jin, Z., Iqbal, M.Z., Bobkov, D., Zou, W., Li, X., Steinbach, E.: A flexible deep CNN framework for image restoration. IEEE Trans. Multimed. 22(4), 1055\u20131068 (2020). https:\/\/doi.org\/10.1109\/TMM.2019.2938340","journal-title":"IEEE Trans. Multimed."},{"key":"4288_CR12","doi-asserted-by":"publisher","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp. 136\u2013144 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.151","DOI":"10.1109\/CVPRW.2017.151"},{"key":"4288_CR13","first-page":"284","volume":"32","author":"S Bell-Kligler","year":"2019","unstructured":"Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-GAN. Adv. Neural Inform. Process. Syst. 32, 284\u2013293 (2019)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"4288_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, K., Van Gool, L., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3214\u20133223 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00328","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"4288_CR15","doi-asserted-by":"publisher","unstructured":"Luo, Z., Huang, H., Yu, L., Li, Y., Fan, H., Liu, S.: Deep constrained least squares for blind image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 17642\u201317652 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01712","DOI":"10.1109\/CVPR52688.2022.01712"},{"key":"4288_CR16","doi-asserted-by":"publisher","unstructured":"Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1604\u20131613 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00170","DOI":"10.1109\/CVPR.2019.00170"},{"issue":"473","key":"4288_CR17","first-page":"5632","volume":"33","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Li, S., Wang, L., Tan, T., et al.: Unfolding the alternating optimization for blind super resolution. Adv. Neural Inform. Process. Syst. 33(473), 5632\u20135643 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"4288_CR18","unstructured":"Luo, Z., Huang, Y., Li, S., Wang, L., Tan, T.: End-to-end alternating optimization for blind super resolution (2021). http:\/\/arxiv.org\/abs\/2105.06878"},{"key":"4288_CR19","doi-asserted-by":"publisher","unstructured":"Kim, S.Y., Sim, H., Kim, M.: Koalanet: Blind super-resolution using kernel-oriented adaptive local adjustment. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10606\u201310615 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01047","DOI":"10.1109\/CVPR46437.2021.01047"},{"key":"4288_CR20","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/tmm.2022.3152090","volume":"25","author":"F Li","year":"2023","unstructured":"Li, F., Wu, Y., Bai, H., Lin, W., Cong, R., Zhao, Y.: Learning detail-structure alternative optimization for blind super-resolution. IEEE Trans. Multimed. 25, 2825\u20132838 (2023). https:\/\/doi.org\/10.1109\/tmm.2022.3152090","journal-title":"IEEE Trans. Multimed."},{"issue":"3","key":"4288_CR21","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/tcsvt.2023.3297673","volume":"34","author":"Y Qiu","year":"2024","unstructured":"Qiu, Y., Zhu, Q., Zhu, S., Zeng, B.: Dual circle contrastive learning-based blind image s-uper-resolution. IEEE Trans. Circuits Syst. Video Technol. 34(3), 1757\u20131771 (2024). https:\/\/doi.org\/10.1109\/tcsvt.2023.3297673","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4288_CR22","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces (2023). http:\/\/arxiv.org\/abs\/2312.00752"},{"key":"4288_CR23","doi-asserted-by":"publisher","unstructured":"Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., Qiao, Y., Ouyang, W., Dong, C.: DiffBIR: toward blind image restoration with generative diffusion prior. In: Proceedings of the European conference on computer vision, pp. 430\u2013448. Springer, (2024). https:\/\/doi.org\/10.1007\/978-3-031-73202-7_25","DOI":"10.1007\/978-3-031-73202-7_25"},{"key":"4288_CR24","doi-asserted-by":"publisher","unstructured":"Kong, L., Dong, J., Ge, J., Li, M., Pan, J.: Efficient frequency domain-based transformers for high-quality image deblurring. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5886\u20135895 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00570","DOI":"10.1109\/CVPR52729.2023.00570"},{"key":"4288_CR25","doi-asserted-by":"publisher","unstructured":"Zhao, C., Cai, W., Dong, C., Hu, C.: Wavelet-based Fourier information interaction with frequency diffusion adjustment for underwater image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8281\u20138291 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.00791","DOI":"10.1109\/CVPR52733.2024.00791"},{"key":"4288_CR26","doi-asserted-by":"crossref","unstructured":"Tan, J., Pei, S., Qin, W., Fu, B., Li, X., Huang, L.: Wavelet-based Mamba with Fourier adjustment for low-light image enhancement. In: Asian conference on computer vision, pp. 160\u2013175 (2024)","DOI":"10.1007\/978-981-96-0911-6_10"},{"key":"4288_CR27","doi-asserted-by":"publisher","unstructured":"Wang, Z., Yan, Z., Yang, J.: SGNet: Structure guided network via gradient-frequency awareness for depth map super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol. 38, pp. 5823\u20135831 (2024). https:\/\/doi.org\/10.1609\/aaai.v38i6.28395","DOI":"10.1609\/aaai.v38i6.28395"},{"key":"4288_CR28","doi-asserted-by":"crossref","unstructured":"Liu, X., Tang, H.: DiffFNO: diffusion Fourier neural operator. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 150\u2013160 (2025)","DOI":"10.1109\/CVPR52734.2025.00023"},{"key":"4288_CR29","unstructured":"Gu, A., Goel, K., R\u00e9, C.: Efficiently modeling long sequences with structured state spaces (2021). http:\/\/arxiv.org\/abs\/2111.00396"},{"key":"4288_CR30","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., Xia, S.-T.: MambaIR: a simple baseline for image restoration with state-space model. In: Proceedings of the european conference on computer vision, pp. 222\u2013241. Springer, (2024)","DOI":"10.1007\/978-3-031-72649-1_13"},{"key":"4288_CR31","doi-asserted-by":"publisher","unstructured":"Guo, H., Guo, Y., Zha, Y., Zhang, Y., Li, W., Dai, T., Xia, S.-T., Li, Y.: MambaIRv2: attentive state space restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 28124\u201328133 (2025). https:\/\/doi.org\/10.1109\/CVPR52734.2025.02619","DOI":"10.1109\/CVPR52734.2025.02619"},{"key":"4288_CR32","doi-asserted-by":"publisher","unstructured":"Li, K., Li, X., Wang, Y., He, Y., Wang, Y., Wang, L., Qiao, Y.: VideoMamba: state space model for efficient video understanding. In: European conference on computer vision, pp. 237\u2013255 (2024). https:\/\/doi.org\/10.1007\/978-3-031-73347-5_1","DOI":"10.1007\/978-3-031-73347-5_1"},{"key":"4288_CR33","doi-asserted-by":"publisher","unstructured":"Nam, J.-H., Syazwany, N.S., Kim, S.J., Lee, S.-C.: Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi-scale attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11480\u201311491 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.01091","DOI":"10.1109\/CVPR52733.2024.01091"},{"key":"4288_CR34","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp. 766\u2013776 (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00092","DOI":"10.1109\/CVPRW56347.2022.00092"},{"key":"4288_CR35","doi-asserted-by":"crossref","unstructured":"Tatsunami, Y., Taki, M.: FFT-Based Dynamic Token Mixer for Vision. In Proceedings of the AAAI conference on artificial intelligence, vol.38, pp. 15328\u201315336 (2024)","DOI":"10.1609\/aaai.v38i14.29457"},{"key":"4288_CR36","doi-asserted-by":"publisher","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp. 126\u2013135 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.150","DOI":"10.1109\/CVPRW.2017.150"},{"key":"4288_CR37","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\/CVF conference on computer vision and pattern recognition workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"4288_CR38","doi-asserted-by":"publisher","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.-L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British machine vision conference, pp. 1\u201310 (2012). https:\/\/doi.org\/10.5244\/C.26.135","DOI":"10.5244\/C.26.135"},{"key":"4288_CR39","doi-asserted-by":"publisher","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and surfaces, pp. 711\u2013730 (2012). https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"4288_CR40","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 of the IEEE\/CVF international conference on computer vision, pp. 416\u2013423 (2001). https:\/\/doi.org\/10.1109\/ICCV.2001.937655","DOI":"10.1109\/ICCV.2001.937655"},{"key":"4288_CR41","doi-asserted-by":"publisher","unstructured":"Huang, J.-B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5197\u20135206 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7299156","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"4","key":"4288_CR42","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":"4288_CR43","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. Multimed Tools Appl. 76, 21811\u201321838 (2017). https:\/\/doi.org\/10.1007\/s11042-016-4020-z","journal-title":"Multimed Tools Appl."},{"key":"4288_CR44","doi-asserted-by":"publisher","unstructured":"Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European conference on computer vision, pp. 252\u2013268 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_16","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"4288_CR45","doi-asserted-by":"publisher","unstructured":"Shocher, A., Cohen, N., Irani, M.: Zero-shot super-resolution using deep internal learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3118\u20133126 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00329","DOI":"10.1109\/CVPR.2018.00329"},{"key":"4288_CR46","unstructured":"Xia, B., Zhang, Y., Wang, Y., Tian, Y., Yang, W., Timofte, R., Van Gool, L.: Knowledge distillation based degradation estimation for blind super-resolution. In: International conference on learning representations (2023)"},{"key":"4288_CR47","doi-asserted-by":"publisher","first-page":"105364","DOI":"10.1016\/j.imavis.2024.105364","volume":"154","author":"A Esmaeilzehi","year":"2025","unstructured":"Esmaeilzehi, A., Babaei, A.M., Nooshi, F., Zaredar, H., Ahmad, M.O.: CLBSR: a deep curriculum learning-based blind image super-resolution network using geometrical prior. Image Vis. Comput. 154, 105364 (2025)","journal-title":"Image Vis. Comput."},{"key":"4288_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image Super-Resolution Using Very Deep Residual Channel Attention Networks. In: Proceedings of the European conference on computer vision (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"4288_CR49","doi-asserted-by":"crossref","unstructured":"Jo, Y., Oh, S.\u202fW., Vajda, P., Kim, S.\u202fJ.: Tackling the Ill\u2011Posedness of Super\u2011Resolution through Adaptive Target Generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 16236\u201316245 (2021)","DOI":"10.1109\/CVPR46437.2021.01597"},{"key":"4288_CR50","doi-asserted-by":"crossref","unstructured":"Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.: Restormer: Efficient Transformer for High-Resolution Image Restoration.\u00a0In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 5718\u20135729 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"4288_CR51","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"4288_CR52","doi-asserted-by":"publisher","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3262\u20133271 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00344","DOI":"10.1109\/CVPR.2018.00344"},{"key":"4288_CR53","doi-asserted-by":"publisher","unstructured":"Zhang, K., Liang, J., Van Gool, L., Timofte, R.: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution. In:\u00a0Proceedings of the IEEE\/CVF international conference on computer vision, pp. 4771\u20134780 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00475","DOI":"10.1109\/ICCV48922.2021.00475"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04288-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04288-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04288-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:00:15Z","timestamp":1772629215000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04288-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4288"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04288-8","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"12 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"57"}}