{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:35:15Z","timestamp":1775874915246,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00530-025-02047-2","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T07:21:42Z","timestamp":1764832902000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Opfusion: a deep blind image super resolution network using generative diffusion models and neural operator learning"],"prefix":"10.1007","volume":"32","author":[{"given":"Morteza","family":"Poudineh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Esmaeilzehi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Omair","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"2047_CR1","doi-asserted-by":"crossref","unstructured":"Ward, C.M., Harguess, J., Crabb, B., Parameswaran, S.: Image quality assessment for determining efficacy and limitations of super-resolution convolutional neural network (srcnn). In: Applications of Digital Image Processing XL, vol. 10396, pp. 19\u201330 (2017). SPIE","DOI":"10.1117\/12.2275157"},{"key":"2047_CR2","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2047_CR3","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 (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"2047_CR4","volume":"94","author":"A Esmaeilzehi","year":"2021","unstructured":"Esmaeilzehi, A., Ahmad, M.O., Swamy, M.: Murnet: A deep recursive network for super resolution of bicubically interpolated images. Signal Process.: Image Commun. 94, 116228 (2021)","journal-title":"Signal Process.: Image Commun."},{"key":"2047_CR5","doi-asserted-by":"crossref","unstructured":"Li, Y., Agustsson, E., Gu, S., Timofte, R., Van\u00a0Gool, L.: Carn: Convolutional anchored regression network for fast and accurate single image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0\u20130 (2018)","DOI":"10.1007\/978-3-030-11021-5_11"},{"key":"2047_CR6","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.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.19"},{"issue":"2","key":"2047_CR7","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s00530-025-01714-8","volume":"31","author":"J Zhang","year":"2025","unstructured":"Zhang, J., Zhu, Y., Peng, S., Niu, A., Yan, Q., Sun, J., Zhang, Y.: A multi-scale feature cross-dimensional interaction network for stereo image super-resolution. Multimedia Syst. 31(2), 114 (2025)","journal-title":"Multimedia Syst."},{"issue":"1","key":"2047_CR8","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s00530-025-01668-x","volume":"31","author":"J Wang","year":"2025","unstructured":"Wang, J., Jin, C., Zhou, S.: Segmentation-aware image super-resolution with generative adversarial networks. Multimedia Syst. 31(1), 82 (2025)","journal-title":"Multimedia Syst."},{"issue":"2","key":"2047_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00530-025-01711-x","volume":"31","author":"C Zhang","year":"2025","unstructured":"Zhang, C., Wang, J., Shi, Y., Yin, B., Ling, N.: A cnn-transformer hybrid network with selective fusion and dual attention for image super-resolution. Multimedia Syst. 31(2), 1\u201317 (2025)","journal-title":"Multimedia Syst."},{"key":"2047_CR10","doi-asserted-by":"crossref","unstructured":"Esmaeilzehi, A., Nooshi, F., Zaredar, H., Ahmad, M.O.: Dhbsr: A deep hybrid representation-based network for blind image super resolution. Computer Vision and Image Understanding, 104034 (2024)","DOI":"10.1016\/j.cviu.2024.104034"},{"key":"2047_CR11","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change\u00a0Loy, C.: Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0\u20130 (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"2047_CR12","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: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"issue":"1","key":"2047_CR13","first-page":"1","volume":"31","author":"W Zhang","year":"2025","unstructured":"Zhang, W., Li, H., Ke, W.: Lf-gianet: cascaded global-view information adaptation-guided network for light field image super-resolution. Multimedia Syst. 31(1), 1\u201315 (2025)","journal-title":"Multimedia Syst."},{"key":"2047_CR14","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 Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"issue":"5","key":"2047_CR15","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/s00530-025-01934-y","volume":"31","author":"A Yang","year":"2025","unstructured":"Yang, A., Yu, C., Wang, J., Wei, Z., Cao, J., Liu, L.: Cross-scale atomic feature enhanced network for high-fidelity single image super-resolution. Multimedia Syst. 31(5), 358 (2025)","journal-title":"Multimedia Syst."},{"issue":"3","key":"2047_CR16","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/s00530-025-01784-8","volume":"31","author":"L Zhang","year":"2025","unstructured":"Zhang, L., Zhang, M., Fan, F., Liu, Y.: Mixed multi-scale residual attention networks for single image super-resolution reconstruction. Multimedia Syst. 31(3), 194 (2025)","journal-title":"Multimedia Syst."},{"key":"2047_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, Y., Zhang, Y., Chai, X., Cheng, Z., Qin, Y., Yang, Y., Xie, R., Song, L.: Enhanced semantic extraction and guidance for ugc image super resolution. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 1421\u20131430 (2025)","DOI":"10.1109\/CVPRW67362.2025.00131"},{"key":"2047_CR18","doi-asserted-by":"crossref","unstructured":"Chen, J., Pan, J., Dong, J.: Faithdiff: Unleashing diffusion priors for faithful image super-resolution. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 28188\u201328197 (2025)","DOI":"10.1109\/CVPR52734.2025.02625"},{"key":"2047_CR19","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"2047_CR20","doi-asserted-by":"crossref","unstructured":"Esmaeilzehi, A., Ahmad, M.O., Swamy, M.: Mghcnet: A deep multi-scale granular and holistic channel feature generation network for image super resolution. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2020). IEEE","DOI":"10.1109\/ICME46284.2020.9102784"},{"key":"2047_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision, pp. 649\u2013667 (2022). Springer","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"2047_CR22","doi-asserted-by":"crossref","unstructured":"Luo, X., Xie, Y., Zhang, Y., Qu, Y., Li, C., Fu, Y.: Latticenet: Towards lightweight image super-resolution with lattice block. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXII 16, pp. 272\u2013289 (2020). Springer","DOI":"10.1007\/978-3-030-58542-6_17"},{"key":"2047_CR23","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723\u2013731 (2018)","DOI":"10.1109\/CVPR.2018.00082"},{"key":"2047_CR24","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, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"2047_CR25","doi-asserted-by":"crossref","unstructured":"Esmaeilzehi, A., Rajabi, M.J., Zaredar, H., Ahmad, M.O.: Discrossformer: A deep light-weight image super resolution network using disentangled visual signal processing and correlated cross-attention transformer operation. In: 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1\u20136 (2024). IEEE","DOI":"10.1109\/MLSP58920.2024.10734781"},{"key":"2047_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262\u20133271 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"2047_CR27","doi-asserted-by":"crossref","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)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"2047_CR28","doi-asserted-by":"crossref","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 (CVPR), pp. 17642\u201317652 (2022)","DOI":"10.1109\/CVPR52688.2022.01712"},{"key":"2047_CR29","doi-asserted-by":"crossref","unstructured":"Talreja, J., Chauhan, D.: Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement. In: Metaheuristics-Based Materials Optimization, pp. 197\u2013228. Elsevier, Amsterdam (2025)","DOI":"10.1016\/B978-0-443-29162-3.00007-1"},{"issue":"2","key":"2047_CR30","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/s40747-024-01760-1","volume":"11","author":"J Talreja","year":"2025","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Xtnsr: Xception-based transformer network for single image super resolution. Compl. Intell. Syst. 11(2), 162 (2025)","journal-title":"Compl. Intell. Syst."},{"key":"2047_CR31","doi-asserted-by":"publisher","first-page":"84379","DOI":"10.1109\/ACCESS.2023.3302692","volume":"11","author":"J Talreja","year":"2023","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Dans: Deep attention network for single image super-resolution. IEEE Access 11, 84379\u201384397 (2023)","journal-title":"IEEE Access"},{"key":"2047_CR32","doi-asserted-by":"publisher","first-page":"122624","DOI":"10.1109\/ACCESS.2024.3450300","volume":"12","author":"J Talreja","year":"2024","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Dhtcun: Deep hybrid transformer cnn u network for single-image super-resolution. IEEE Access 12, 122624\u2013122641 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3450300","journal-title":"IEEE Access"},{"key":"2047_CR33","first-page":"9","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 9 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"2047_CR34","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: An overview. IEEE Signal Process. Mag. 35(1), 53\u201365 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"2047_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van\u00a0Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4791\u20134800 (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"2047_CR36","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1905\u20131914 (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"2047_CR37","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2047_CR38","doi-asserted-by":"crossref","unstructured":"Lin, X., He, J., Chen, Z., Lyu, Z., Fei, B., Dai, B., Ouyang, W., Qiao, Y., Dong, C.: Diffbir: Towards blind image restoration with generative diffusion prior. arXiv:2308.15070 (2023)","DOI":"10.1007\/978-3-031-73202-7_25"},{"key":"2047_CR39","doi-asserted-by":"crossref","unstructured":"Wang, J., Yue, Z., Zhou, S., Chan, K.C., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. arXiv:2305.07015 (2023)","DOI":"10.1007\/s11263-024-02168-7"},{"key":"2047_CR40","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00070"},{"key":"2047_CR41","unstructured":"Luo, C.: Understanding diffusion models: A unified perspective. arXiv:2208.11970 (2022)"},{"key":"2047_CR42","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., Guo, Y.: Unsupervised degradation representation learning for blind super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10581\u201310590 (2021)","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"2047_CR43","unstructured":"Zhang, J., Lu, S., Zhan, F., Yu, Y.: Blind image super-resolution via contrastive representation learning. arXiv:2107.00708 (2021)"},{"key":"2047_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, R., Susstrunk, S.: Kernel modeling super-resolution on real low-resolution images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2433\u20132443 (2019)","DOI":"10.1109\/ICCV.2019.00252"},{"key":"2047_CR45","doi-asserted-by":"crossref","unstructured":"Yang, Z., Xia, J., Li, S., Huang, X., Zhang, S., Liu, Z., Fu, Y., Liu, Y.: A dynamic kernel prior model for unsupervised blind image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26046\u201326056 (2024)","DOI":"10.1109\/CVPR52733.2024.02461"},{"key":"2047_CR46","unstructured":"Huo, D., Yang, Y.-H.: Blind image super-resolution with spatial context hallucination. arXiv:2009.12461 (2020)"},{"key":"2047_CR47","doi-asserted-by":"crossref","unstructured":"Yang, T., Ren, P., Xie, X., Zhang, L.: Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv:2308.14469 (2023)","DOI":"10.1007\/978-3-031-73247-8_5"},{"issue":"4","key":"2047_CR48","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1137\/0721048","volume":"21","author":"CW Gear","year":"1984","unstructured":"Gear, C.W., Petzold, L.R.: Ode methods for the solution of differential\/algebraic systems. SIAM J. Numer. Anal. 21(4), 716\u2013728 (1984)","journal-title":"SIAM J. Numer. Anal."},{"key":"2047_CR49","unstructured":"Yang, T., Ren, P., Zhang, L., et al.: Synthesizing realistic image restoration training pairs: A diffusion approach. arXiv:2303.06994 (2023)"},{"key":"2047_CR50","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. 1(2), 3 (2022). arXiv:2204.06125"},{"key":"2047_CR51","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021). PMLR"},{"key":"2047_CR52","doi-asserted-by":"crossref","unstructured":"Ruan, L., Ma, Y., Yang, H., He, H., Liu, B., Fu, J., Yuan, N.J., Jin, Q., Guo, B.: Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10219\u201310228 (2023)","DOI":"10.1109\/CVPR52729.2023.00985"},{"key":"2047_CR53","first-page":"8","volume":"36","author":"M Bellagente","year":"2024","unstructured":"Bellagente, M., Brack, M., Teufel, H., Friedrich, F., Deiseroth, B., Eichenberg, C., Dai, A.M., Baldock, R., Nanda, S., Oostermeijer, K., et al.: Multifusion: Fusing pre-trained models for multi-lingual, multi-modal image generation. Adv. Neural Inf. Process. Syst. 36, 8 (2024)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2047_CR54","unstructured":"Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv:2112.10741 (2021)"},{"key":"2047_CR55","doi-asserted-by":"publisher","first-page":"23716","DOI":"10.52202\/068431-1723","volume":"35","author":"J-B Alayrac","year":"2022","unstructured":"Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Adv. Neural. Inf. Process. Syst. 35, 23716\u201323736 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2047_CR56","doi-asserted-by":"crossref","unstructured":"Kim, G., Kwon, T., Ye, J.C.: Diffusionclip: Text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2426\u20132435 (2022)","DOI":"10.1109\/CVPR52688.2022.00246"},{"key":"2047_CR57","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2047_CR58","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.: Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing 479, 47\u201359 (2022)","journal-title":"Neurocomputing"},{"key":"2047_CR59","first-page":"8","volume":"31","author":"RT Chen","year":"2018","unstructured":"Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. Adv. Neural Inf. Process. Syst. 31, 8 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2047_CR60","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2047_CR61","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2047_CR62","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2047_CR63","doi-asserted-by":"crossref","unstructured":"Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: A new benchmark and a new model. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3086\u20133095 (2019)","DOI":"10.1109\/ICCV.2019.00318"},{"issue":"3","key":"2047_CR64","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"2047_CR65","doi-asserted-by":"crossref","unstructured":"Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., Lin, L.: Component divide-and-conquer for real-world image super-resolution. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VIII 16, pp. 101\u2013117 (2020). Springer","DOI":"10.1007\/978-3-030-58598-3_7"},{"key":"2047_CR66","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"2047_CR67","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 466\u2013467 (2020)","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"2047_CR68","doi-asserted-by":"crossref","unstructured":"Chen, C., Shi, X., Qin, Y., Li, X., Han, X., Yang, T., Guo, S.: Real-world blind super-resolution via feature matching with implicit high-resolution priors. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1329\u20131338 (2022)","DOI":"10.1145\/3503161.3547833"},{"key":"2047_CR69","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Li, Z., Guo, C.-L., Bai, S., Cheng, M.-M., Hou, Q.: Srformer: Permuted self-attention for single image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12780\u201312791 (2023)","DOI":"10.1109\/ICCV51070.2023.01174"},{"key":"2047_CR70","unstructured":"Yue, Z., Wang, J., Loy, C.C.: Resshift: Efficient diffusion model for image super-resolution by residual shifting. arXiv:2307.12348 (2023)"},{"key":"2047_CR71","first-page":"92529","volume":"37","author":"R Wu","year":"2024","unstructured":"Wu, R., Sun, L., Ma, Z., Zhang, L.: One-step effective diffusion network for real-world image super-resolution. Adv. Neural. Inf. Process. Syst. 37, 92529\u201392553 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"2047_CR72","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)","journal-title":"IEEE Trans. Image Process."},{"key":"2047_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"2047_CR74","doi-asserted-by":"crossref","unstructured":"Wang, J., Chan, K.C., Loy, C.C.: Exploring clip for assessing the look and feel of images. In: Proceedings of the AAAI Conference on Artificial Intelligence 37(2), 2555\u20132563 (2023)","DOI":"10.1609\/aaai.v37i2.25353"},{"key":"2047_CR75","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y., Milanfar, P., Yang, F.: Musiq: Multi-scale image quality transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5148\u20135157 (2021)","DOI":"10.1109\/ICCV48922.2021.00510"},{"issue":"8","key":"2047_CR76","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1109\/TIP.2015.2426416","volume":"24","author":"L Zhang","year":"2015","unstructured":"Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579\u20132591 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"2047_CR77","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1109\/TIP.2018.2831899","volume":"27","author":"H Talebi","year":"2018","unstructured":"Talebi, H., Milanfar, P.: Nima: Neural image assessment. IEEE Trans. Image Process. 27(8), 3998\u20134011 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"2047_CR78","doi-asserted-by":"crossref","unstructured":"Murray, N., Marchesotti, L., Perronnin, F.: Ava: A large-scale database for aesthetic visual analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2408\u20132415 (2012). IEEE","DOI":"10.1109\/CVPR.2012.6247954"},{"key":"2047_CR79","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, T., Shi, S., Lao, S., Gong, Y., Cao, M., Wang, J., Yang, Y.: Maniqa: Multi-dimension attention network for no-reference image quality assessment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1191\u20131200 (2022)","DOI":"10.1109\/CVPRW56347.2022.00126"},{"key":"2047_CR80","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733\u20131740 (2014)","DOI":"10.1109\/CVPR.2014.224"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02047-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-02047-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02047-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T08:43:46Z","timestamp":1774428226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-02047-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2047"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-02047-2","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]},"assertion":[{"value":"7 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"16"}}