{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:17:09Z","timestamp":1782152229150,"version":"3.54.5"},"publisher-location":"Cham","reference-count":75,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734137","type":"print"},{"value":"9783031734144","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73414-4_10","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T17:02:54Z","timestamp":1729789374000},"page":"164-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Latent Diffusion Prior Enhanced Deep Unfolding for\u00a0Snapshot Spectral Compressive Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0750-0246","authenticated-orcid":false,"given":"Zongliang","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8825-6064","authenticated-orcid":false,"given":"Ruiying","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-694X","authenticated-orcid":false,"given":"Ying","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-7524","authenticated-orcid":false,"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"issue":"1","key":"10_CR1","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Img. Sci. 2(1), 183\u2013202 (2009)","journal-title":"SIAM J. Img. Sci."},{"issue":"12","key":"10_CR2","first-page":"2992","volume":"16","author":"J Bioucas-Dias","year":"2007","unstructured":"Bioucas-Dias, J., Figueiredo, M.: A new TwIST: two-step iterative shrinkage\/thresholding algorithms for image restoration. IEEE TIP 16(12), 2992\u20133004 (2007)","journal-title":"IEEE TIP"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Blattmann, A., et al.: Align your Latents: high-resolution video synthesis with latent diffusion models. In: CVPR, pp. 22563\u201322575 (2023)","DOI":"10.1109\/CVPR52729.2023.02161"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Cai, Y., et al.: Coarse-to-fine sparse transformer for hyperspectral image reconstruction. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13677, pp. 686\u2013704. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19790-1_41","DOI":"10.1007\/978-3-031-19790-1_41"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Cai, Y., et al.: Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction. In: CVPR, pp. 17502\u201317511 (2022)","DOI":"10.1109\/CVPR52688.2022.01698"},{"key":"10_CR6","unstructured":"Cai, Y., et al.: Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging. In: NeurIPS, pp. 37749\u201337761 (2022)"},{"key":"10_CR7","unstructured":"Cai, Y., Zheng, Y., Lin, J., Yuan, X., Zhang, Y., Wang, H.: Binarized spectral compressive imaging. In: NeurIPS, vol. 36 (2024)"},{"key":"10_CR8","first-page":"84","volume":"3","author":"SH Chan","year":"2017","unstructured":"Chan, S.H., Wang, X., Elgendy, O.A.: Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE TCI 3, 84\u201398 (2017)","journal-title":"IEEE TCI"},{"issue":"5","key":"10_CR9","first-page":"963","volume":"5","author":"AS Charles","year":"2011","unstructured":"Charles, A.S., Olshausen, B.A., Rozell, C.J.: Learning sparse codes for hyperspectral imagery. IEEE JSTSP 5(5), 963\u2013978 (2011)","journal-title":"IEEE JSTSP"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gui, X., Zeng, J., Zhao, X.L., He, W.: Combining low-rank and deep plug-and-play priors for snapshot compressive imaging. IEEE TNNLS (2023)","DOI":"10.1109\/TNNLS.2023.3294262"},{"key":"10_CR11","first-page":"926","volume":"33","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Lai, W., He, W., Zhao, X.L., Zeng, J.: Hyperspectral compressive snapshot reconstruction via coupled low-rank subspace representation and self-supervised deep network. IEEE TIP 33, 926\u2013941 (2024)","journal-title":"IEEE TIP"},{"key":"10_CR12","unstructured":"Chen, Z., et al.: Hierarchical integration diffusion model for realistic image deblurring. arXiv preprint arXiv:2305.12966 (2023)"},{"issue":"2","key":"10_CR13","doi-asserted-by":"publisher","first-page":"2264","DOI":"10.1109\/TPAMI.2022.3161934","volume":"45","author":"Z Cheng","year":"2022","unstructured":"Cheng, Z., et al.: Recurrent neural networks for snapshot compressive imaging. IEEE TPAMI 45(2), 2264\u20132281 (2022)","journal-title":"IEEE TPAMI"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Choi, I., Jeon, D.S., Nam, G., Gutierrez, D., Kim, M.H.: High-quality hyperspectral reconstruction using a spectral prior. ACM TOG 36(6), 218:1\u2013218:13 (2017)","DOI":"10.1145\/3130800.3130810"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Dong, Y., Gao, D., Qiu, T., Li, Y., Yang, M., Shi, G.: Residual degradation learning unfolding framework with mixing priors across spectral and spatial for compressive spectral imaging. In: CVPR, pp. 22262\u201322271 (2023)","DOI":"10.1109\/CVPR52729.2023.02132"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Gao, S., et al.: Implicit diffusion models for continuous super-resolution. In: CVPR, pp. 10021\u201310030 (2023)","DOI":"10.1109\/CVPR52729.2023.00966"},{"issue":"21","key":"10_CR17","doi-asserted-by":"publisher","first-page":"14013","DOI":"10.1364\/OE.15.014013","volume":"15","author":"ME Gehm","year":"2007","unstructured":"Gehm, M.E., John, R., Brady, D.J., Willett, R.M., Schulz, T.J.: Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Exp. 15(21), 14013\u201314027 (2007)","journal-title":"Opt. Exp."},{"issue":"4704","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","volume":"228","author":"AF Goetz","year":"1985","unstructured":"Goetz, A.F., Vane, G., Solomon, J.E., Rock, B.N.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147\u20131153 (1985)","journal-title":"Science"},{"key":"10_CR19","unstructured":"Harvey, W., Naderiparizi, S., Masrani, V., Weilbach, C., Wood, F.: Flexible diffusion modeling of long videos. In: NeurIPS, vol. 35, pp. 27953\u201327965 (2022)"},{"key":"10_CR20","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)"},{"key":"10_CR21","unstructured":"Ho, J., et\u00a0al.: Imagen video: high definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022)"},{"key":"10_CR22","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"10_CR23","unstructured":"H\u00f6ppe, T., Mehrjou, A., Bauer, S., Nielsen, D., Dittadi, A.: Diffusion models for video prediction and infilling. arXiv preprint arXiv:2206.07696 (2022)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Howard, A., et\u00a0al.: Searching for MobileNetV3. In: ICCV, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Hu, X., et al.: HDNet: high-resolution dual-domain learning for spectral compressive imaging. In: CVPR, pp. 17542\u201317551 (2022)","DOI":"10.1109\/CVPR52688.2022.01702"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Huang, T., Dong, W., Yuan, X., Wu, J., Shi, G.: Deep gaussian scale mixture prior for spectral compressive imaging. In: CVPR, pp. 16216\u201316225 (2021)","DOI":"10.1109\/CVPR46437.2021.01595"},{"key":"10_CR27","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR28","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. CoRR abs\/1312.6114 (2013)"},{"issue":"36","key":"10_CR29","doi-asserted-by":"publisher","first-page":"6824","DOI":"10.1364\/AO.49.006824","volume":"49","author":"D Kittle","year":"2010","unstructured":"Kittle, D., Choi, K., Wagadarikar, A., Brady, D.J.: Multiframe image estimation for coded aperture snapshot spectral imagers. Appl. Opt. 49(36), 6824\u20136833 (2010)","journal-title":"Appl. Opt."},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Lai, Z., Fu, Y., Zhang, J.: Hyperspectral image super resolution with real unaligned RGB guidance. IEEE TNNLS (2024)","DOI":"10.1109\/TNNLS.2023.3340561"},{"issue":"3","key":"10_CR31","first-page":"1037","volume":"33","author":"L Li","year":"2020","unstructured":"Li, L., Li, W., Qu, Y., Zhao, C., Tao, R., Du, Q.: Prior-based tensor approximation for anomaly detection in hyperspectral imagery. IEEE TNNLS 33(3), 1037\u20131050 (2020)","journal-title":"IEEE TNNLS"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Li, M., Fu, Y., Liu, J., Zhang, Y.: Pixel adaptive deep unfolding transformer for hyperspectral image reconstruction. In: ICCV, pp. 12959\u201312968 (2023)","DOI":"10.1109\/ICCV51070.2023.01191"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Li, M., Fu, Y., Zhang, Y.: Spatial-spectral transformer for hyperspectral image denoising. In: AAAI, vol.\u00a037, pp. 1368\u20131376 (2023)","DOI":"10.1609\/aaai.v37i1.25221"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., Fu, Y., Zhang, Y., Dou, D.: Spectral enhanced rectangle transformer for hyperspectral image denoising. In: CVPR, pp. 5805\u20135814 (2023)","DOI":"10.1109\/CVPR52729.2023.00562"},{"issue":"9","key":"10_CR35","first-page":"6690","volume":"57","author":"S Li","year":"2019","unstructured":"Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Deep learning for hyperspectral image classification: an overview. IEEE TGRS 57(9), 6690\u20136709 (2019)","journal-title":"IEEE TGRS"},{"issue":"2","key":"10_CR36","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1137\/130936658","volume":"7","author":"X Liao","year":"2014","unstructured":"Liao, X., Li, H., Carin, L.: Generalized alternating projection for weighted-2,1 minimization with applications to model-based compressive sensing. SIAM J. Imag. Sci. 7(2), 797\u2013823 (2014)","journal-title":"SIAM J. Imag. Sci."},{"issue":"12","key":"10_CR37","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1109\/TPAMI.2018.2873587","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Yuan, X., Suo, J., Brady, D., Dai, Q.: Rank minimization for snapshot compressive imaging. IEEE TPAMI 41(12), 2990\u20133006 (2019)","journal-title":"IEEE TPAMI"},{"issue":"1","key":"10_CR38","doi-asserted-by":"publisher","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1), 010901 (2014)","journal-title":"J. Biomed. Opt."},{"key":"10_CR39","doi-asserted-by":"publisher","first-page":"107737","DOI":"10.1016\/j.sigpro.2020.107737","volume":"177","author":"R Lu","year":"2020","unstructured":"Lu, R., Chen, B., Cheng, Z., Wang, P.: RAFnet: recurrent attention fusion network of hyperspectral and multispectral images. Sig. Process. 177, 107737 (2020)","journal-title":"Sig. Process."},{"issue":"4","key":"10_CR40","first-page":"649","volume":"16","author":"R Lu","year":"2022","unstructured":"Lu, R., et al.: Heterogeneity-aware recurrent neural network for hyperspectral and multispectral image fusion. IEEE JSTSP 16(4), 649\u2013665 (2022)","journal-title":"IEEE JSTSP"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van\u00a0Gool, L.: RePaint: inpainting using denoising diffusion probabilistic models. In: CVPR, pp. 11461\u201311471 (2022)","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Ma, J., Liu, X.Y., Shou, Z., Yuan, X.: Deep tensor ADMM-Net for snapshot compressive imaging. In: ICCV, pp. 10223\u201310232 (2019)","DOI":"10.1109\/ICCV.2019.01032"},{"key":"10_CR43","unstructured":"Meng, Z., Jalali, S., Yuan, X.: GAP-net for snapshot compressive imaging. arXiv preprint arXiv:2012.08364 (2020)"},{"key":"10_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-030-58592-1_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Meng","year":"2020","unstructured":"Meng, Z., Ma, J., Yuan, X.: End-to-end low cost compressive spectral imaging with spatial-spectral self-attention. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 187\u2013204. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_12"},{"key":"10_CR45","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1007\/s11263-023-01844-4","volume":"131","author":"Z Meng","year":"2023","unstructured":"Meng, Z., Yuan, X., Jalali, S.: Deep unfolding for snapshot compressive imaging. Int. J. Comput. Vis. 131, 2933\u20132958 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Miao, X., Yuan, X., Pu, Y., Athitsos, V.: $$\\lambda $$-net: reconstruct hyperspectral images from a snapshot measurement. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00416"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., Nayar, S.K.: Multispectral imaging using multiplexed illumination. In: 2007 IEEE 11th ICCV, pp.\u00a01\u20138. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"10_CR48","doi-asserted-by":"crossref","unstructured":"Qiu, H., Wang, Y., Meng, D.: Effective snapshot compressive-spectral imaging via deep denoising and total variation priors. In: CVPR, pp. 9127\u20139136 (2021)","DOI":"10.1109\/CVPR46437.2021.00901"},{"key":"10_CR49","first-page":"1","volume":"60","author":"W Rao","year":"2022","unstructured":"Rao, W., Gao, L., Qu, Y., Sun, X., Zhang, B., Chanussot, J.: Siamese transformer network for hyperspectral image target detection. IEEE TGRS 60, 1\u201319 (2022)","journal-title":"IEEE TGRS"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"ul\u00a0Rehman, A., Qureshi, S.A.: A review of the medical hyperspectral imaging systems and unmixing algorithms\u2019 in biological tissues. Photodiagn. Photodyn. Ther. 33, 102165 (2021)","DOI":"10.1016\/j.pdpdt.2020.102165"},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Ren, M., Delbracio, M., Talebi, H., Gerig, G., Milanfar, P.: Multiscale structure guided diffusion for image deblurring. In: ICCV, pp. 10721\u201310733 (2023)","DOI":"10.1109\/ICCV51070.2023.00984"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10_CR53","unstructured":"Ryu, E., Liu, J., Wang, S., Chen, X., Wang, Z., Yin, W.: Plug-and-play methods provably converge with properly trained denoisers. In: ICML, pp. 5546\u20135557 (2019)"},{"issue":"4","key":"10_CR54","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE TPAMI 45(4), 4713\u20134726 (2022)","journal-title":"IEEE TPAMI"},{"key":"10_CR55","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, pp. 2256\u20132265 (2015)"},{"key":"10_CR56","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"10_CR57","unstructured":"Tolstikhin, I.O., et\u00a0al.: MLP-mixer: an all-MLP architecture for vision. In: NeurIPS, pp. 24261\u201324272 (2021)"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Rangnekar, A., Hoffman, M.: Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps. In: CVPR Workshops, pp. 39\u201348 (2017)","DOI":"10.1109\/CVPRW.2017.35"},{"key":"10_CR59","doi-asserted-by":"crossref","unstructured":"Van\u00a0Nguyen, H., Banerjee, A., Chellappa, R.: Tracking via object reflectance using a hyperspectral video camera. In: CVPR Workshops, pp. 44\u201351 (2010)","DOI":"10.1109\/CVPRW.2010.5543780"},{"issue":"10","key":"10_CR60","doi-asserted-by":"publisher","first-page":"B44","DOI":"10.1364\/AO.47.000B44","volume":"47","author":"A Wagadarikar","year":"2008","unstructured":"Wagadarikar, A., John, R., Willett, R., Brady, D.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47(10), B44\u2013B51 (2008)","journal-title":"Appl. Opt."},{"issue":"10","key":"10_CR61","doi-asserted-by":"publisher","first-page":"B44","DOI":"10.1364\/AO.47.000B44","volume":"47","author":"A Wagadarikar","year":"2008","unstructured":"Wagadarikar, A., John, R., Willett, R., Brady, D.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47(10), B44\u2013B51 (2008)","journal-title":"Appl. Opt."},{"issue":"10","key":"10_CR62","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.1109\/TPAMI.2016.2621050","volume":"39","author":"L Wang","year":"2017","unstructured":"Wang, L., Xiong, Z., Shi, G., Wu, F., Zeng, W.: Adaptive nonlocal sparse representation for dual-camera compressive hyperspectral imaging. IEEE TPAMI 39(10), 2104\u20132111 (2017)","journal-title":"IEEE TPAMI"},{"issue":"8","key":"10_CR63","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1364\/PRJ.458231","volume":"10","author":"L Wang","year":"2022","unstructured":"Wang, L., Wu, Z., Zhong, Y., Yuan, X.: Snapshot spectral compressive imaging reconstruction using convolution and contextual transformer. Photon. Res. 10(8), 1848\u20131858 (2022)","journal-title":"Photon. Res."},{"key":"10_CR64","doi-asserted-by":"crossref","unstructured":"Wang, L., Sun, C., Zhang, M., Fu, Y., Huang, H.: DNU: deep non-local unrolling for computational spectral imaging. In: CVPR, pp. 1661\u20131671 (2020)","DOI":"10.1109\/CVPR42600.2020.00173"},{"key":"10_CR65","first-page":"7565","volume":"29","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Chen, B., Lu, R., Zhang, H., Liu, H., Varshney, P.K.: FusionNet: an unsupervised convolutional variational network for hyperspectral and multispectral image fusion. IEEE TIP 29, 7565\u20137577 (2020)","journal-title":"IEEE TIP"},{"issue":"4","key":"10_CR66","first-page":"600","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 TIP 13(4), 600\u2013612 (2004)","journal-title":"IEEE TIP"},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"Xia, B., et al.: DiffIR: efficient diffusion model for image restoration. arXiv preprint arXiv:2303.09472 (2023)","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"10_CR68","first-page":"2838","volume":"26","author":"P Xu","year":"2023","unstructured":"Xu, P., Liu, L., Zheng, H., Yuan, X., Xu, C., Xue, L.: Degradation-aware dynamic Fourier-based network for spectral compressive imaging. IEEE TMM 26, 2838\u20132850 (2023)","journal-title":"IEEE TMM"},{"key":"10_CR69","doi-asserted-by":"crossref","unstructured":"Yuan, X.: Generalized alternating projection based total variation minimization for compressive sensing. In: ICIP, pp. 2539\u20132543 (2016)","DOI":"10.1109\/ICIP.2016.7532817"},{"key":"10_CR70","doi-asserted-by":"crossref","unstructured":"Yuan, X., Liu, Y., Suo, J., Dai, Q.: Plug-and-play algorithms for large-scale snapshot compressive imaging. In: CVPR, June 2020 (2020)","DOI":"10.1109\/CVPR42600.2020.00152"},{"key":"10_CR71","doi-asserted-by":"publisher","first-page":"7093","DOI":"10.1109\/TPAMI.2021.3099035","volume":"44","author":"X Yuan","year":"2021","unstructured":"Yuan, X., Liu, Y., Suo, J., Durand, F., Dai, Q.: Plug-and-play algorithms for video snapshot compressive imaging. IEEE TPAMI 44, 7093\u20137111 (2021)","journal-title":"IEEE TPAMI"},{"key":"10_CR72","unstructured":"Zhang, Q., Chen, Y.: Fast sampling of diffusion models with exponential integrator. arXiv preprint arXiv:2204.13902 (2022)"},{"key":"10_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, L., Fu, Y., Zhong, X., Huang, H.: Computational hyperspectral imaging based on dimension-discriminative low-rank tensor recovery. In: ICCV, pp. 10183\u201310192 (2019)","DOI":"10.1109\/ICCV.2019.01028"},{"key":"10_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, T., Fu, Y., Li, C.: Hyperspectral image denoising with realistic data. In: ICCV, pp. 2248\u20132257 (2021)","DOI":"10.1109\/ICCV48922.2021.00225"},{"issue":"4","key":"10_CR75","first-page":"636","volume":"16","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Liang, Z., Fu, Y.: Joint spatial-spectral pattern optimization and hyperspectral image reconstruction. IEEE JSTSP 16(4), 636\u2013648 (2022)","journal-title":"IEEE JSTSP"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73414-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T17:07:16Z","timestamp":1729789636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73414-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031734137","9783031734144"],"references-count":75,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73414-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}