{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:24:11Z","timestamp":1761953051113,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533978","type":"print"},{"value":"9789819533985","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3398-5_31","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:39Z","timestamp":1761952779000},"page":"379-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["End-to-End Diffusion Models with\u00a0Physics Priors for\u00a0Enhanced Spectral Super-Resolution"],"prefix":"10.1007","author":[{"given":"Xinxin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"31_CR1","unstructured":"Wan, Y., et al.: UAV-ground hyperspectral monitoring of tailings reservoir disasters: a case study in Xinjiang. In: IGARSS, pp. 9713\u20139716 (2019)"},{"key":"31_CR2","first-page":"1","volume":"60","author":"J Jia","year":"2022","unstructured":"Jia, J., et al.: Tradeoffs in spatial and spectral resolution of airborne hyperspectral systems: a crop identification case. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"31_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113000","volume":"275","author":"JM Meyer","year":"2022","unstructured":"Meyer, J.M., Kokaly, R.F., Holley, E.: Hyperspectral sensing of white mica: a review with spectrometer design insights. Remote Sens. Environ. 275, 113000 (2022)","journal-title":"Remote Sens. Environ."},{"issue":"11","key":"31_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"31_CR5","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: NeurIPS, vol. 34, pp. 8780\u20138794 (2021)"},{"key":"31_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-46478-7_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"B Arad","year":"2016","unstructured":"Arad, B., Ben-Shahar, O.: Sparse recovery of hyperspectral signal from natural RGB images. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 19\u201334. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_2"},{"key":"31_CR7","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.neucom.2017.08.019","volume":"273","author":"L Fang","year":"2018","unstructured":"Fang, L., Zhuo, H., Li, S.: Super-resolution of hyperspectral image via superpixel-based sparse representation. Neurocomputing 273, 171\u2013177 (2018)","journal-title":"Neurocomputing"},{"key":"31_CR8","unstructured":"Galliani, S., Lanaras, C., Marmanis, D., Baltsavias, E., Schindler, K.: Learned spectral super-resolution. arXiv:1703.09470 (2017)"},{"key":"31_CR9","doi-asserted-by":"publisher","first-page":"10059","DOI":"10.1109\/TNNLS.2023.3238506","volume":"35","author":"R Dian","year":"2023","unstructured":"Dian, R., Shan, T., He, W., Liu, H.: Spectral super-resolution via model-guided cross-fusion network. IEEE Trans. Neural Netw. Learn Syst 35, 10059\u201310070 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn Syst"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y.-B., Xia, S.-T., Zhang, L.: Second-order attention network for single image super-resolution. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 11057\u201311066 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"5532616","DOI":"10.1109\/TGRS.2023.3335975","volume":"61","author":"L Liu","year":"2023","unstructured":"Liu, L., Chen, B., Chen, H., Zou, Z., Shi, Z.: Diverse hyperspectral remote sensing image synthesis with diffusion models. IEEE Trans. Geosci. Remote Sens. 61, 5532616 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"31_CR12","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, pp. 6840\u20136851 (2020)"},{"key":"31_CR13","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2021)"},{"key":"31_CR14","unstructured":"Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. In: Advances in Neural Information Processing Systems (2022)"},{"issue":"4","key":"31_CR15","first-page":"4713","volume":"45","author":"C Saharia","year":"2023","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3407206","volume":"62","author":"J Zhou","year":"2024","unstructured":"Zhou, J., et al.: Exploring multi-timestep multi-stage diffusion features for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 62, 1\u201316 (2024). https:\/\/doi.org\/10.1109\/TGRS.2024.3407206","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"31_CR17","doi-asserted-by":"publisher","unstructured":"Bandara, W.G.C., Nair, N.G., Patel, V.M.: Remote sensing change detection using denoising diffusion probabilistic models. arXiv:2206.11892 (2022). https:\/\/doi.org\/10.48550\/ARXIV.2206.11892","DOI":"10.48550\/ARXIV.2206.11892"},{"key":"31_CR18","unstructured":"Wolleb, J., Sandk\u00fchler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. arXiv:2112.03145 (2021)"},{"key":"31_CR19","unstructured":"Wang, Y., Yu, J., Zhang, J.: Zero-shot image restoration using denoising diffusion null-space model. In: International Conference on Learning Representations (2023)"},{"issue":"1","key":"31_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends Mach. Learn."},{"issue":"2","key":"31_CR21","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/TPAMI.2013.102","volume":"36","author":"R He","year":"2014","unstructured":"He, R., Zheng, W.-S., Tan, T., Sun, Z.: Half-quadratic-based iterative minimization for robust sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 261\u2013275 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR22","doi-asserted-by":"publisher","first-page":"3992","DOI":"10.1109\/TPAMI.2025.3538896","volume":"47","author":"B Chen","year":"2025","unstructured":"Chen, B., et al.: Invertible diffusion models for compressed sensing. IEEE Trans. Pattern Anal. Mach. Intell. 47, 3992\u20134006 (2025)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"31_CR23","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.1109\/TIP.2010.2046811","volume":"19","author":"F Yasuma","year":"2010","unstructured":"Yasuma, F., Mitsunaga, T., Iso, D., Nayar, S.K.: Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum. IEEE Trans. Image Process. 19(9), 2241\u20132253 (2010)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"31_CR24","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/LGRS.2004.837009","volume":"1","author":"F Dell\u2019Acqua","year":"2004","unstructured":"Dell\u2019Acqua, F., Gamba, P., Ferrari, A., Palmason, J.A., Benediktsson, J.A., \u00c1rnason, K.: Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geosci. Remote Sens. Lett. 1(4), 322\u2013326 (2004)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"31_CR25","first-page":"49","volume":"66","author":"T Ranchin","year":"2000","unstructured":"Ranchin, T., Wald, L.: Fusion of high spatial and spectral resolution images: the arsis concept and its implementation. Photogramm. Eng. Remote. Sens. 66(1), 49\u201361 (2000)","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Pixel-aware deep function-mixture network for spectral super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12821\u201312828 (2020)","DOI":"10.1609\/aaai.v34i07.6978"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Cai, Y., et al.: MST++: multi-stage spectral-wise transformer for efficient spectral reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 744\u2013754 (2022)","DOI":"10.1109\/CVPRW56347.2022.00090"},{"key":"31_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3225843","volume":"60","author":"X Zheng","year":"2022","unstructured":"Zheng, X., Chen, W., Lu, X.: Spectral super-resolution of multispectral images using spatial-spectral residual attention network. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"31_CR29","doi-asserted-by":"publisher","first-page":"5140","DOI":"10.1109\/TNNLS.2024.3359852","volume":"36","author":"R Dian","year":"2025","unstructured":"Dian, R., Liu, Y., Li, S.: Spectral super-resolution via deep low-rank tensor representation. IEEE Trans. Neural Netw. Learn. Syst. 36(3), 5140\u20135150 (2025)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3398-5_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:42Z","timestamp":1761952782000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3398-5_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533978","9789819533985"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3398-5_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}