{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:55:36Z","timestamp":1776956136260,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819756087","type":"print"},{"value":"9789819756094","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5609-4_30","type":"book-chapter","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T12:02:04Z","timestamp":1722340924000},"page":"392-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["RSSRDiff: An Effective Diffusion Probability Model with Attention for Single Remote Sensing Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Tian","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"issue":"9","key":"30_CR1","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.3390\/rs15092346","volume":"15","author":"AM Ali","year":"2023","unstructured":"Ali, A.M., Benjdira, B., et al.: Tesr: two-stage approach for enhancement and super-resolution of remote sensing images. Remote Sensing 15(9), 2346 (2023)","journal-title":"Remote Sensing"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"An, T., Xue, B., Huo, C., Xiang, S., Pan, C.: Efficient remote sensing image super-resolution via lightweight diffusion models. IEEE Geosci. Remote Sensing Lett. (2023)","DOI":"10.1109\/LGRS.2023.3335421"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Chung, H., et al.: Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12413\u201312422 (2022)","DOI":"10.1109\/CVPR52688.2022.01209"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell.\u00a038(2), 295\u2013307 (2015)","DOI":"10.1109\/TPAMI.2015.2439281"},{"issue":"13","key":"30_CR5","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.3390\/rs15133452","volume":"15","author":"L Han","year":"2023","unstructured":"Han, L., et al.: Enhancing remote sensing image super-resolution with efficient hybrid conditional diffusion model. Remote Sensing 15(13), 3452 (2023)","journal-title":"Remote Sensing"},{"key":"30_CR6","unstructured":"Shen, F., et al.: Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models, arXiv preprint arXiv:2310.06313, (2023)"},{"key":"30_CR7","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., et al.: Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing 479, 47\u201359 (2022)","journal-title":"Neurocomputing"},{"issue":"19","key":"30_CR8","doi-asserted-by":"publisher","first-page":"4834","DOI":"10.3390\/rs14194834","volume":"14","author":"J Liu","year":"2022","unstructured":"Liu, J., et al.: Diffusion model with detail complement for super-resolution of remote sensing. Remote Sensing 14(19), 4834 (2022)","journal-title":"Remote Sensing"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Shen, F., et al.: Git: graph interactive transformer for vehicle re-identification. IEEE Trans. Image Process.\u00a032, 1039--1051 (2023)","DOI":"10.1109\/TIP.2023.3238642"},{"issue":"4","key":"30_CR10","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., et al.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR11","unstructured":"Shen, F., et al.: Triplet Contrastive Learning for Unsupervised Vehicle Re-identification. arXiv 2023. arXiv preprint arXiv:2301.09498 (2023)"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"5662","DOI":"10.1109\/JSTARS.2022.3190322","volume":"15","author":"J Tu","year":"2022","unstructured":"Tu, J., Mei, G., Ma, Z., Piccialli, F.: Swcgan: Generative adversarial network combining swin transformer and cnn for remote sensing image super-resolution. IEEE J. Selected Topics Appli. Earth Observations Remote Sensing 15, 5662\u20135673 (2022)","journal-title":"IEEE J. Selected Topics Appli. Earth Observations Remote Sensing"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Wang, X., et al.: Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018).\u00a0https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"30_CR15","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018).\u00a0https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"7","key":"30_CR16","doi-asserted-by":"publisher","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","volume":"55","author":"GS Xia","year":"2017","unstructured":"Xia, G.S., et al.: Aid: A benchmark data set for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965\u20133981 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"30_CR17","unstructured":"Xia, G.S., et al.: Structural high- resolution satellite image indexing. In: ISPRS TC VII Symposium-100 Years IS- PRS, vol. 38, pp. 298\u2013303 (2010)"},{"key":"30_CR18","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.inffus.2023.03.021","volume":"96","author":"Y Xiao","year":"2023","unstructured":"Xiao, Y., et al.: From degrade to upgrade: learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution. Inform. Fusion 96, 297\u2013311 (2023)","journal-title":"Inform. Fusion"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Epsanet: An efficient pyramid squeeze attention block on convolutional neural network. In: Proceedings of the Asian Conference on Computer Vision, pp. 1161\u20131177 (2022)","DOI":"10.1007\/978-3-031-26313-2_33"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, L., Tang, P., et al.: Feature significance-based multibag-of-visual-words model for remote sensing image scene classification. J. Appli. Remote Sensing 10(3), 035004\u2013035004 (2016)","DOI":"10.1117\/1.JRS.10.035004"},{"issue":"11","key":"30_CR21","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","volume":"12","author":"Q Zou","year":"2015","unstructured":"Zou, Q., Ni, L., et al.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321\u20132325 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"30_CR22","unstructured":"Yao, J., et al.: Improving depth gradient continuity in transformers: a comparative study on monocular depth estimation with cnn. arXiv preprint arXiv:2308.08333 (2023)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5609-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T02:15:13Z","timestamp":1732500913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5609-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756087","9789819756094"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5609-4_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}