{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T01:43:51Z","timestamp":1784943831804,"version":"3.55.0"},"publisher-location":"Cham","reference-count":108,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197895","type":"print"},{"value":"9783031197901","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19790-1_41","type":"book-chapter","created":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T11:02:44Z","timestamp":1666522964000},"page":"686-704","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Coarse-to-Fine Sparse Transformer for\u00a0Hyperspectral Image Reconstruction"],"prefix":"10.1007","author":[{"given":"Yuanhao","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radu","family":"Timofte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"issue":"6791","key":"41_CR1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/35017638","volume":"406","author":"V Backman","year":"2000","unstructured":"Backman, V., et al.: Detection of preinvasive cancer cells. Nature 406(6791), 35\u201336 (2000)","journal-title":"Nature"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., Veit, A.: Understanding robustness of transformers for image classification. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias, J., Figueiredo., M.: A new twist: Two-step iterative shrinkage\/thresholding algorithms for image restoration. TIP, 16(12), 2992\u20133004 (2007)","DOI":"10.1109\/TIP.2007.909319"},{"key":"41_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/9781420012606","volume-title":"Hyperspectral Remote Sensing: Principles and Applications","author":"M Borengasser","year":"2007","unstructured":"Borengasser, M., Hungate, W.S., Watkins, R.: Hyperspectral Remote Sensing: Principles and Applications. CRC Press, Boca Raton (2007)"},{"key":"41_CR5","unstructured":"Cai, Y., Hu, X., Wang, H., Zhang, Y., Pfister, H., Wei, D.: Learning to generate realistic noisy images via pixel-level noise-aware adversarial training. In: NeurIPS (2021)"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Cai, Y., et al.: Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01698"},{"key":"41_CR7","unstructured":"Cai, Y., et al.: Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging. arXiv preprint arXiv:2205.10102 (2022)"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Y., et al.: Learning delicate local representations for multi-person pose estimation. arXiv preprint arXiv:2003.04030 (2020)","DOI":"10.1007\/978-3-030-58580-8_27"},{"key":"41_CR9","unstructured":"Cao, J., Li, Y., Zhang, K., Van Gool, L.: Video super-resolution transformer. arXiv preprint arXiv:2106.06847 (2021)"},{"issue":"12","key":"41_CR10","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1109\/TPAMI.2011.80","volume":"33","author":"X Cao","year":"2011","unstructured":"Cao, X., Du, H., Tong, X., Dai, Q., Lin, S.: A prism-mask system for multispectral video acquisition. TPAMI 33(12), 2423\u20132435 (2011)","journal-title":"TPAMI"},{"issue":"5","key":"41_CR11","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/MSP.2016.2582378","volume":"33","author":"X Cao","year":"2016","unstructured":"Cao, X., et al.: Computational snapshot multispectral cameras: toward dynamic capture of the spectral world. Signal Process. Mag. 33(5), 95\u2013108 (2016)","journal-title":"Signal Process. Mag."},{"key":"41_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"issue":"1","key":"41_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/TCI.2016.2629286","volume":"3","author":"SH Chan","year":"2016","unstructured":"Chan, S.H., Wang, X., Elgendy, O.A.: Plug-and-play ADMM for image restoration: fixed-point convergence and applications. Transactions on Computational Imaging 3(1), 84\u201398 (2016)","journal-title":"Transactions on Computational Imaging"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Chen, C.F.R., Fan, Q., Panda, R.: CrossVIT: cross-attention multi-scale vision transformer for image classification. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"41_CR16","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"41_CR17","unstructured":"Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: NeurIPS (2021)"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Choi, I., Kim, M., Gutierrez, D., Jeon, D., Nam, G.: High-quality hyperspectral reconstruction using a spectral prior. In: Technical report (2017)","DOI":"10.1145\/3130800.3130810"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., Zhang, L.: Dynamic DETR: end-to-end object detection with dynamic attention. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00298"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Dai, Z., Cai, B., Lin, Y., Chen, J.: UP-DETR: unsupervised pre-training for object detection with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00165"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Deng, Z., et al.: RFormer: transformer-based generative adversarial network for real fundus image restoration on a new clinical benchmark. arXiv preprint arXiv:2201.00466 (2022)","DOI":"10.1109\/JBHI.2022.3187103"},{"key":"41_CR22","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"41_CR23","unstructured":"El-Nouby, A., et al.: XCiT: Cross-covariance image transformers. arXiv preprint arXiv:2106.09681 (2021)"},{"key":"41_CR24","unstructured":"Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: CVPR (2006)"},{"key":"41_CR25","unstructured":"Fang, Y., et al.: You only look at one sequence: rethinking transformer in vision through object detection. In: NeurIPS (2021)"},{"issue":"3","key":"41_CR26","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","volume":"101","author":"M Fauvel","year":"2012","unstructured":"Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 101(3), 652\u2013675 (2012)","journal-title":"Proc. IEEE"},{"issue":"4","key":"41_CR27","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/JSTSP.2007.910281","volume":"1","author":"MA Figueiredo","year":"2007","unstructured":"Figueiredo, M.A., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Sign. Process. 1(4), 586\u2013597 (2007)","journal-title":"IEEE J. Sel. Top. Sign. Process."},{"key":"41_CR28","doi-asserted-by":"crossref","unstructured":"Fu, Y., Liang, Z., You, S.: Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution. J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 2674\u20132688 (2021)","DOI":"10.1109\/JSTARS.2021.3057936"},{"key":"41_CR29","doi-asserted-by":"crossref","unstructured":"Fu, Y., Zhang, T., Wang, L., Huang, H.: Coded hyperspectral image reconstruction using deep external and internal learning. TPAMI, 44(7) (2021)","DOI":"10.1109\/TPAMI.2021.3059911"},{"key":"41_CR30","doi-asserted-by":"crossref","unstructured":"Fu, Y., Zheng, Y., Sato, I., Sato, Y.: Exploiting spectral-spatial correlation for coded hyperspectral image restoration. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.405"},{"issue":"21","key":"41_CR31","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. Express 15(21), 14013\u201314027 (2007)","journal-title":"Opt. Express"},{"key":"41_CR32","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. In: NeurIPS (2021)"},{"key":"41_CR33","doi-asserted-by":"crossref","unstructured":"Hu, X., et al.: HDNET: high-resolution dual-domain learning for spectral compressive imaging. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01702"},{"key":"41_CR34","doi-asserted-by":"crossref","unstructured":"Hu, X., Cai, Y., Liu, Z., Wang, H., Zhang, Y.: Multi-scale selective feedback network with dual loss for real image denoising. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/101"},{"key":"41_CR35","doi-asserted-by":"crossref","unstructured":"Hu, X., et al.: Pseudo 3D auto-correlation network for real image denoising. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01591"},{"key":"41_CR36","doi-asserted-by":"crossref","unstructured":"Hu, X., Wang, H., Cai, Y., Zhao, X., Zhang, Y.: Pyramid orthogonal attention network based on dual self-similarity for accurate MR image super-resolution. In: ICME (2021)","DOI":"10.1109\/ICME51207.2021.9428112"},{"key":"41_CR37","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 (2021)","DOI":"10.1109\/CVPR46437.2021.01595"},{"key":"41_CR38","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511534799","volume-title":"Spectrograph Design Fundamentals","author":"J James","year":"2007","unstructured":"James, J.: Spectrograph Design Fundamentals. Cambridge University Press, Cambridge (2007)"},{"key":"41_CR39","doi-asserted-by":"crossref","unstructured":"Jiang, T., Camgoz, N.C., Bowden, R.: Skeletor: skeletal transformers for robust body-pose estimation. In: CVPR (2021)","DOI":"10.1109\/CVPRW53098.2021.00378"},{"issue":"4","key":"41_CR40","first-page":"1","volume":"31","author":"MH Kim","year":"2012","unstructured":"Kim, M.H., et al.: 3D imaging spectroscopy for measuring hyperspectral patterns on solid objects. ACM Trans. Graph. 31(4), 1\u201311 (2012)","journal-title":"ACM Trans. Graph."},{"key":"41_CR41","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"41_CR42","unstructured":"Kitaev, N., Kaiser, \u0141., Levskaya, A.: Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451 (2020)"},{"key":"41_CR43","doi-asserted-by":"crossref","unstructured":"Lanchantin, J., Wang, T., Ordonez, V., Qi, Y.: General multi-label image classification with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"41_CR44","unstructured":"Li, W., Liu, H., Ding, R., Liu, M., Wang, P.: Lifting transformer for 3D human pose estimation in video. arXiv preprint arXiv:2103.14304 (2021)"},{"key":"41_CR45","unstructured":"Li, X., Zhang, L., You, A., Yang, M., Yang, K., Tong, Y.: Global aggregation then local distribution in fully convolutional networks. In: BMVC (2019)"},{"key":"41_CR46","unstructured":"Li, Y., Hao, M., Di, Z., Gundavarapu, N.B., Wang, X.: Test-time personalization with a transformer for human pose estimation. In: NeurIPS (2021)"},{"key":"41_CR47","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: ICCVW (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"41_CR48","unstructured":"Lin, J., et al.: Flow-guided sparse transformer for video deblurring. arXiv preprint arXiv:2201.01893 (2022)"},{"key":"41_CR49","doi-asserted-by":"crossref","unstructured":"Lin, K., Wang, L., Liu, Z.: End-to-end human pose and mesh reconstruction with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00199"},{"key":"41_CR50","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yuan, X., Suo, J., Brady, D., Dai, Q.: Rank minimization for snapshot compressive imaging. TPAMI (2019)","DOI":"10.1109\/TPAMI.2018.2873587"},{"issue":"12","key":"41_CR51","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1109\/TPAMI.2018.2873587","volume":"41","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Yuan, X., Suo, J., Brady, D.J., Dai, Q.: Rank minimization for snapshot compressive imaging. TPAMI 41(12), 2990\u20133006 (2018)","journal-title":"TPAMI"},{"key":"41_CR52","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"41_CR53","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"issue":"1","key":"41_CR54","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":"41_CR55","doi-asserted-by":"crossref","unstructured":"Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.Z., Xiang, T.: Simpler is better: few-shot semantic segmentation with classifier weight transformer. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00862"},{"key":"41_CR56","doi-asserted-by":"crossref","unstructured":"Ludwig, K., Harzig, P., Lienhart, R.: Detecting arbitrary intermediate keypoints for human pose estimation with vision transformers. In: WACV (2022)","DOI":"10.1109\/WACVW54805.2022.00073"},{"key":"41_CR57","doi-asserted-by":"crossref","unstructured":"Ma, J., Liu, X.Y., Shou, Z., Yuan, X.: Deep tensor admm-net for snapshot compressive imaging. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01032"},{"issue":"9","key":"41_CR58","doi-asserted-by":"publisher","first-page":"4962","DOI":"10.1109\/TGRS.2017.2697453","volume":"55","author":"E Maggiori","year":"2017","unstructured":"Maggiori, E., Charpiat, G., Tarabalka, Y., Alliez, P.: Recurrent neural networks to correct satellite image classification maps. Trans. Geosci. Remote Sens. 55(9), 4962\u20134971 (2017)","journal-title":"Trans. Geosci. Remote Sens."},{"key":"41_CR59","doi-asserted-by":"crossref","unstructured":"Manakov, A., et al.: A reconfigurable camera add-on for high dynamic range, multispectral, polarization, and light-field imaging. Trans. Graph. (2013)","DOI":"10.1145\/2461912.2461937"},{"key":"41_CR60","doi-asserted-by":"crossref","unstructured":"Mao, W., Ge, Y., Shen, C., Tian, Z., Wang, X., Wang, Z.: TFPOSE: direct human pose estimation with transformers. arXiv preprint arXiv:2103.15320 (2021)","DOI":"10.1007\/978-3-031-20068-7_5"},{"key":"41_CR61","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00352"},{"issue":"8","key":"41_CR62","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. Trans. Geosci. Remote Sens. 42(8), 1778\u20131790 (2004)","journal-title":"Trans. Geosci. Remote Sens."},{"key":"41_CR63","unstructured":"Meng, Z., Jalali, S., Yuan, X.: Gap-net for snapshot compressive imaging. arXiv preprint arXiv:2012.08364 (2020)"},{"key":"41_CR64","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"},{"issue":"14","key":"41_CR65","doi-asserted-by":"publisher","first-page":"3897","DOI":"10.1364\/OL.393213","volume":"45","author":"Z Meng","year":"2020","unstructured":"Meng, Z., Qiao, M., Ma, J., Yu, Z., Xu, K., Yuan, X.: Snapshot multispectral endomicroscopy. Opt. Lett. 45(14), 3897\u20133900 (2020)","journal-title":"Opt. Lett."},{"key":"41_CR66","doi-asserted-by":"crossref","unstructured":"Meng, Z., Yu, Z., Xu, K., Yuan, X.: Self-supervised neural networks for spectral snapshot compressive imaging. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00262"},{"key":"41_CR67","doi-asserted-by":"crossref","unstructured":"Miao, X., Yuan, X., Pu, Y., Athitsos, V.: l-net: reconstruct hyperspectral images from a snapshot measurement. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00416"},{"key":"41_CR68","doi-asserted-by":"crossref","unstructured":"Misra, I., Girdhar, R., Joulin, A.: An end-to-end transformer model for 3D object detection. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00290"},{"key":"41_CR69","doi-asserted-by":"crossref","unstructured":"Pan, X., Xia, Z., Song, S., Li, L.E., Huang, G.: 3D object detection with pointformer. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00738"},{"key":"41_CR70","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., Nayar, S.K.: Multispectral imaging using multiplexed illumination. In: ICCV (2007)","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"41_CR71","unstructured":"Patrick, W., Hirsch, M., Scholkopf, B., Lensch, H.P.A.: Learning blind motion deblurring. In: ICCV (2017)"},{"issue":"7","key":"41_CR72","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1364\/OL.386238","volume":"45","author":"M Qiao","year":"2020","unstructured":"Qiao, M., Liu, X., Yuan, X.: Snapshot spatial-temporal compressive imaging. Opt. Lett. 45(7), 1659\u20131662 (2020)","journal-title":"Opt. Lett."},{"key":"41_CR73","unstructured":"Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. In: NeurIPS (2019)"},{"issue":"4704","key":"41_CR74","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","volume":"228","author":"J Solomon","year":"1985","unstructured":"Solomon, J., Rock, B.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147\u20131153 (1985)","journal-title":"Science"},{"key":"41_CR75","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"41_CR76","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Hoffman, M.J., Vodacek, A.: Real-time vehicle tracking in aerial video using hyperspectral features. In: CVPRW (2016)","DOI":"10.1109\/CVPRW.2016.181"},{"key":"41_CR77","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Rangnekar, A., Hoffman, M.: Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.35"},{"key":"41_CR78","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"issue":"10","key":"41_CR79","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."},{"key":"41_CR80","doi-asserted-by":"crossref","unstructured":"Wang, L., Wu, Z., Zhong, Y., Yuan, X.: Spectral compressive imaging reconstruction using convolution and spectral contextual transformer. arXiv preprint arXiv:2201.05768 (2022)","DOI":"10.1364\/PRJ.458231"},{"key":"41_CR81","doi-asserted-by":"crossref","unstructured":"Wang, L., Sun, C., Fu, Y., Kim, M.H., Huang, H.: Hyperspectral image reconstruction using a deep spatial-spectral prior. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00822"},{"key":"41_CR82","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 (2020)","DOI":"10.1109\/CVPR42600.2020.00173"},{"issue":"4","key":"41_CR83","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1364\/AO.54.000848","volume":"54","author":"L Wang","year":"2015","unstructured":"Wang, L., Xiong, Z., Gao, D., Shi, G., Wu, F.: Dual-camera design for coded aperture snapshot spectral imaging. Appl. Opt. 54(4), 848\u2013858 (2015)","journal-title":"Appl. Opt."},{"key":"41_CR84","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Liu, J.: Uformer: a general u-shaped transformer for image restoration. arXiv preprint 2106.03106 (2021)","DOI":"10.1109\/CVPR52688.2022.01716"},{"issue":"4","key":"41_CR85","first-page":"600","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncell, E.P.: Image quality assessment: from error visibility to structural similarity. TIP 13(4), 600\u2013612 (2004)","journal-title":"TIP"},{"key":"41_CR86","unstructured":"Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677 (2020)"},{"key":"41_CR87","doi-asserted-by":"crossref","unstructured":"Wu, K., Peng, H., Chen, M., Fu, J., Chao, H.: Rethinking and improving relative position encoding for vision transformer. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"41_CR88","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: NeurIPS (2021)"},{"key":"41_CR89","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Shi, Z., Li, H., Wang, L., Liu, D., Wu, F.: HSCNN: CNN-based hyperspectral image recovery from spectrally undersampled projections. In: ICCVW (2017)","DOI":"10.1109\/ICCVW.2017.68"},{"issue":"8","key":"41_CR90","first-page":"3467","volume":"21","author":"J Yang","year":"2012","unstructured":"Yang, J., Wang, Z., Lin, Z., Cohen, S., Huang, T.: Coupled dictionary training for image super-resolution. TIP 21(8), 3467\u20133478 (2012)","journal-title":"TIP"},{"issue":"11","key":"41_CR91","first-page":"2861","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. TIP 19(11), 2861\u20132873 (2010)","journal-title":"TIP"},{"key":"41_CR92","unstructured":"Yang, J., et al.: Focal self-attention for local-global interactions in vision transformers. arXiv preprint arXiv:2107.00641 (2021)"},{"key":"41_CR93","unstructured":"Yang, Z., Wei, Y., Yang, Y.: Associating objects with transformers for video object segmentation. In: NeurIPS (2021)"},{"key":"41_CR94","doi-asserted-by":"crossref","unstructured":"Yuan, X.: Generalized alternating projection based total variation minimization for compressive sensing. In: ICIP (2016)","DOI":"10.1109\/ICIP.2016.7532817"},{"key":"41_CR95","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 (2020)","DOI":"10.1109\/CVPR42600.2020.00152"},{"key":"41_CR96","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Zheng, X., Lu, X.: Hyperspectral image superresolution by transfer learning. J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(5), 1963\u20131974 (2017)","DOI":"10.1109\/JSTARS.2017.2655112"},{"key":"41_CR97","unstructured":"Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., Wang, J.: HRFormer: high-resolution transformer for dense prediction. In: NeurIPS (2021)"},{"key":"41_CR98","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. ArXiv 2111.09881 (2021)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"41_CR99","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: CycleISP: real image restoration via improved data synthesis. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00277"},{"key":"41_CR100","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1007\/978-3-030-58595-2_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"SW Zamir","year":"2020","unstructured":"Zamir, S.W., et al.: Learning enriched features for real image restoration and enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 492\u2013511. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_30"},{"key":"41_CR101","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"issue":"3","key":"41_CR102","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","volume":"54","author":"F Zhang","year":"2015","unstructured":"Zhang, F., Du, B., Zhang, L.: Scene classification via a gradient boosting random convolutional network framework. Trans. Geosci. Remote Sens. 54(3), 1793\u20131802 (2015)","journal-title":"Trans. Geosci. Remote Sens."},{"key":"41_CR103","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: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"41_CR104","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: ICLR (2019)"},{"issue":"10","key":"41_CR105","first-page":"2057","volume":"27","author":"C Zhao","year":"2016","unstructured":"Zhao, C., Zhang, J., Ma, S., Fan, X., Zhang, Y., Gao, W.: Reducing image compression artifacts by structural sparse representation and quantization constraint prior. TCSVT 27(10), 2057\u20132071 (2016)","journal-title":"TCSVT"},{"key":"41_CR106","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., Ding, Z.: 3D human pose estimation with spatial and temporal transformers. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"41_CR107","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"41_CR108","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19790-1_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:18:29Z","timestamp":1710256709000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19790-1_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197895","9783031197901"],"references-count":108,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19790-1_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}