{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:29:25Z","timestamp":1767421765894,"version":"3.48.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shaanxi Province Qin Chuangyuan \"Scientist+Engineer\" Team Construction Project","award":["2024QCY-KXJ-168  and 2024QCY-KXJ-196."],"award-info":[{"award-number":["2024QCY-KXJ-168  and 2024QCY-KXJ-196."]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-05044-0","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T07:12:24Z","timestamp":1766560344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PPIRB:generate prior prompt information to dynamically adjust the high-resolution reconstruction process"],"prefix":"10.1007","volume":"19","author":[{"given":"Guibao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiheng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyuan","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuzhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"5044_CR1","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"issue":"11","key":"5044_CR2","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861\u20132873 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"5044_CR3","doi-asserted-by":"crossref","unstructured":"Osendorfer, C., Soyer, H., Smagt, P.: Image super-resolution with fast approximate convolutional sparse coding. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 250\u2013257 (2014)","DOI":"10.1007\/978-3-319-12643-2_31"},{"key":"5044_CR4","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Lecture Notes in Computer Science (LNCS), pp. 391\u2013407 (2016)","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"5044_CR5","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), pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"5044_CR6","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"5044_CR7","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"},{"key":"5044_CR8","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: ECCV Workshops, pp. 41\u201355 (2020)","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"5044_CR9","doi-asserted-by":"crossref","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 (CVPRW), pp. 765\u2013775 (2022)","DOI":"10.1109\/CVPRW56347.2022.00092"},{"issue":"1","key":"5044_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10514-021-09998-1","volume":"46","author":"RA Rosu","year":"2022","unstructured":"Rosu, R.A., Sch\u00fctt, P., Quenzel, J., Behnke, S.: Latticenet: Fast spatio-temporal point cloud segmentation using permutohedral lattices. Auton. Robot. 46(1), 45\u201360 (2022)","journal-title":"Auton. Robot."},{"key":"5044_CR11","doi-asserted-by":"crossref","unstructured":"Lee, M.-K., Heo, J.-P.: Noise-free optimization in early training steps for image super-resolution. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 2920\u20132928 (2024)","DOI":"10.1609\/aaai.v38i4.28073"},{"key":"5044_CR12","doi-asserted-by":"crossref","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105\u2013114 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"5044_CR13","doi-asserted-by":"crossref","unstructured":"Ma, C.: Uncertainty-aware gan for single image super-resolution. In: AAAI Conference on Artificial Intelligence (AAAI), vol. 38, pp. 4071\u20134079 (2024)","DOI":"10.1609\/aaai.v38i5.28201"},{"key":"5044_CR14","doi-asserted-by":"crossref","unstructured":"Korkmaz, C., Tekalp, A.M., Dogan, Z.: Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5926\u20135936 (2024)","DOI":"10.1109\/CVPR52733.2024.00566"},{"key":"5044_CR15","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"5044_CR16","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"5044_CR17","doi-asserted-by":"crossref","unstructured":"Ray, A., Kumar, G., Kolekar, M.H.: Cfat: Unleashing triangular windows for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26120\u201326129 (2024)","DOI":"10.1109\/CVPR52733.2024.02468"},{"issue":"1","key":"5044_CR18","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1609\/aaai.v36i1.19946","volume":"36","author":"W Li","year":"2022","unstructured":"Li, W., et al.: Feature distillation interaction weighting network for lightweight image super-resolution. Proceedings of the AAAI Conference on Artificial Intelligence 36(1), 661\u2013669 (2022)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5044_CR19","doi-asserted-by":"crossref","unstructured":"Li, W., et al.: Efficient image super-resolution with feature interaction weighted hybrid network. IEEE Transactions on Multimedia (2024)","DOI":"10.1109\/TMM.2024.3521753"},{"key":"5044_CR20","doi-asserted-by":"crossref","unstructured":"Gao, G., et al.: Contextual transformation network for lightweight remote-sensing image super-resolution. IEEE Trans. Geosci. Remote Sens. 61, 1\u201313 (2023)","DOI":"10.1109\/TGRS.2023.3275135"},{"key":"5044_CR21","doi-asserted-by":"crossref","unstructured":"Gao, G., et al.: Lightweight bimodal network for single-image super-resolution via symmetric cnn and recursive transformer. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), pp. 913\u2013919 (2022)","DOI":"10.24963\/ijcai.2022\/128"},{"key":"5044_CR22","unstructured":"Li, W., Guo, H., Hou, Y., Gao, G., Ma, Z.: Dual-domain modulation network for lightweight image super-resolution. IEEE Transactions on Multimedia (2024)"},{"key":"5044_CR23","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Gool, L.V., Timofte, R.: Fourier space losses for efficient perceptual image super-resolution. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 2340\u20132349 (2021)","DOI":"10.1109\/ICCV48922.2021.00236"},{"key":"5044_CR24","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132\u20131140 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"5044_CR25","first-page":"359","volume-title":"Computer Vision - ECCV 2024","author":"M Zheng","year":"2024","unstructured":"Zheng, M., Sun, L., Dong, J., Pan, J.: Smfanet: A lightweight self-modulation feature aggregation network for efficient image super-resolution. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision - ECCV 2024, pp. 359\u2013375. Springer, Cham (2024)"},{"key":"5044_CR26","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 (CVPR), pp. 9199\u20139208 (2021)","DOI":"10.1109\/CVPR46437.2021.00908"},{"key":"5044_CR27","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. arXiv preprint arXiv:1701.04128 (2017)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-05044-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-05044-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-05044-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:23:56Z","timestamp":1767421436000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-05044-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":27,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["5044"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-05044-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"17 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2025","order":4,"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":"Competing interests"}}],"article-number":"1458"}}