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Cand\u00e8s, J. Romberg, and T. Tao, \u201cRobust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,\u201d IEEE Trans. Inf. Theory, vol.52, no.2, pp.489-509, 2006, 10.1109\/TIT.2005.862083. 10.1109\/tit.2005.862083","DOI":"10.1109\/TIT.2005.862083"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] D.L. Donoho, \u201cCompressed sensing,\u201d IEEE Trans. Inf. Theory, vol.52, no.4, pp.1289-1306, 2006, 10.1109\/TIT.2006.871582. 10.1109\/tit.2006.871582","DOI":"10.1109\/TIT.2006.871582"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] H.J. Landau, \u201cSampling, data transmission, and the Nyquist rate,\u201d Proc. IEEE, vol.55, no.10, pp.1701-1706, 1967, 10.1109\/PROC. 1967.5962. 10.1109\/proc.1967.5962","DOI":"10.1109\/PROC.1967.5962"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] E.J. Candes, \u201cThe restricted isometry property and its implications for compressed sensing,\u201d Comptes rendus mathematique, vol.346, no.9-10, pp.589-592, 2008, 10.1016\/j.crma.2008.03.014. 10.1016\/j.crma.2008.03.014","DOI":"10.1016\/j.crma.2008.03.014"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] E.J. Cand\u00e8s and T. Tao, \u201cNear-optimal signal recovery from random projections: Universal encoding strategies,\u201d IEEE Trans. Inf. Theory, vol.52, no.12, pp.5406-5425, 2006, 10.1109\/TIT.2006.885507. 10.1109\/tit.2006.885507","DOI":"10.1109\/TIT.2006.885507"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Y.M. Lu and M.N. Do, \u201cA theory for sampling signals from a union of subspaces,\u201d IEEE Trans. Signal Process., vol.56, no.6, pp.2334-2345, 2008, 10.1109\/TSP.2007.914346. 10.1109\/tsp.2007.914346","DOI":"10.1109\/TSP.2007.914346"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] T. Blumensath, \u201cSampling and reconstructing signals from a union of linear subspaces,\u201d IEEE Trans. Inf. Theory, vol.57, no.7, pp.4660-4671, 2011, 10.1109\/TIT.2011.2146550. 10.1109\/tit.2011.2146550","DOI":"10.1109\/TIT.2011.2146550"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y.C. Eldar and M. Mishali, \u201cRobust recovery of signals from a structured union of subspaces,\u201d IEEE Trans. Inf. Theory, vol.55, no.11, pp.5302-5316, 2009, 10.1109\/TIT.2009.2030471. 10.1109\/tit.2009.2030471","DOI":"10.1109\/TIT.2009.2030471"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] Y.C. Eldar, P. Kuppinger, and H. Bolcskei, \u201cBlock-sparse signals: uncertainty relations and efficient recovery,\u201d IEEE Trans. Signal Process., vol.58, no.6, pp.3042-3054, 2010, 10.1109\/TSP.2010. 2044837. 10.1109\/tsp.2010.2044837","DOI":"10.1109\/TSP.2010.2044837"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A. Mousavi, A.B. Patel, and R.G. Baraniuk, \u201cA deep learning approach to structured signal recovery,\u201d 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, pp.1336-1343, 2015, 10.1109\/ ALLERTON.2015.7447163. 10.1109\/allerton.2015.7447163","DOI":"10.1109\/ALLERTON.2015.7447163"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Mousavi and R.G. Baraniuk, \u201cLearning to invert: Signal recovery via deep convolutional networks,\u201d 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp.2272-2276, 2017, 10.1109\/ICASSP.2017.7952561. 10.1109\/icassp.2017.7952561","DOI":"10.1109\/ICASSP.2017.7952561"},{"key":"12","unstructured":"[12] X. Lu, W. Dong, P. Wang, et al., \u201cConvCSNet: A convolutional compressive sensing framework based on deep learning,\u201d arXiv preprint arXiv:1801.10342, 2018."},{"key":"13","unstructured":"[13] H. Yao, F. Dai, D. Zhang, et al., \u201cDR2-net: Deep residual reconstruction network for image compressive sensing,\u201d arXiv preprint arXiv:1702.05743, 2017."},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] U.S. Kamilov and H. Mansour, \u201cLearning optimal nonlinearities for iterative thresholding algorithms,\u201d IEEE Signal Process. Lett., vol.23, no.5, pp.747-751, 2016, 10.1109\/LSP.2016.2548245. 10.1109\/lsp.2016.2548245","DOI":"10.1109\/LSP.2016.2548245"},{"key":"15","unstructured":"[15] C.A. Metzler, A. Mousavi, and R.G. Baraniuk, \u201cLearned D-AMP: Principled neural network based compressive image recovery,\u201d arXiv preprint arXiv:1704.06625, 2017."},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] B. Girod, A.M. Aaron, S. Rane, and D. Rebollo-Monedero, \u201cDistributed video coding,\u201d Proc. IEEE, vol.93, no.1, pp.71-83, 2005, 10.1109\/JPROC.2004.839619. 10.1109\/jproc.2004.839619","DOI":"10.1109\/JPROC.2004.839619"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] F. Pereira, L. Torres, C. Guillemot, T. Ebrahimi, R. Leonardi, and S. Klomp, \u201cDistributed video coding: selecting the most promising application scenarios,\u201d Signal Processing: Image Communication, vol.23, no.5, pp.339-352, 2008, 10.1016\/j.image.2008.04.002. 10.1016\/j.image.2008.04.002","DOI":"10.1016\/j.image.2008.04.002"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] L. Gan, \u201cBlock compressed sensing of natural images,\u201d Proc. international conference on digital signal processing, IEEE, pp.403-406, 2007, 10.1109\/ICDSP.2007.4288604. 10.1109\/icdsp.2007.4288604","DOI":"10.1109\/ICDSP.2007.4288604"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] J. Zhang, D. Zhao, and W. Gao, \u201cGroup-based sparse representation for image restoration,\u201d IEEE Trans. Image Process., vol.23, no.8, pp.3336-3351, 2014, 10.1109\/TIP.2014.2323127. 10.1109\/tip.2014.2323127","DOI":"10.1109\/TIP.2014.2323127"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] J. Zhang, C. Zhao, D. Zhao, and W. Gao, \u201cImage compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization,\u201d Signal Processing, vol.103, pp.114-126, 2014, 10.1016\/j.sigpro.2013.09.025. 10.1016\/j.sigpro.2013.09.025","DOI":"10.1016\/j.sigpro.2013.09.025"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] P. Llull, X. Liao, X. Yuan, J. Yang, D. Kittle, L. Carin, G. Sapiro, and D.J. Brady, \u201cCoded aperture compressive temporal imaging,\u201d Optics express, vol.21, no.9, pp.10526-10545, 2013, 10.1364\/OE.21.010526. 10.1364\/oe.21.010526","DOI":"10.1364\/OE.21.010526"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] S. Mun and J.E. Fowler, \u201cResidual reconstruction for block-based compressed sensing of video,\u201d Data Compression Conference (DCC), IEEE, pp.183-192, 2011, 10.1109\/DCC.2011.25. 10.1109\/dcc.2011.25","DOI":"10.1109\/DCC.2011.25"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] C. Zhao, S. Ma, J. Zhang, R. Xiong, and W. Gao, \u201cVideo compressive sensing reconstruction via reweighted residual sparsity,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.27, no.6, pp.1182-1195, 2017, 10.1109\/TCSVT.2016.2527181. 10.1109\/tcsvt.2016.2527181","DOI":"10.1109\/TCSVT.2016.2527181"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] K. Chang, P.L.K. Ding, and B. Li, \u201cCompressive sensing reconstruction of correlated images using joint regularization,\u201d IEEE Signal Process. Lett., vol.23, no.4, pp.449-453, 2016, 10.1109\/LSP.2016.2527680. 10.1109\/lsp.2016.2527680","DOI":"10.1109\/LSP.2016.2527680"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] J. Chen, N. Wang, F. Xue, and Y. Gao, \u201cDistributed compressed video sensing based on the optimization of hypothesis set update technique,\u201d Multimedia Tools and Applications, vol.76, no.14, pp.15735-15754, 2017, 10.1007\/s11042-016-3866-4. 10.1007\/s11042-016-3866-4","DOI":"10.1007\/s11042-016-3866-4"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] Y. Kuo, K. Wu, and J. Chen, \u201cA scheme for distributed compressed video sensing based on hypothesis set optimization techniques,\u201d Multidimensional Systems and Signal Processing, vol.28, no.1, pp.129-148, 2017, 10.1007\/s11045-015-0337-4. 10.1007\/s11045-015-0337-4","DOI":"10.1007\/s11045-015-0337-4"},{"key":"27","unstructured":"[27] T.T. Do, Y. Chen, D.T. Nguyen, N. Nguyen, L. Gan, and T.D. Tran, \u201cDistributed compressed video sensing,\u201d 16th IEEE International Conference on Image Processing (ICIP), IEEE, pp.1393-1396, 2009, 10.1109\/CISS.2009.5054678. 10.1109\/CISS.2009.5054678"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] E.W. Tramel and J.E. Fowler, \u201cVideo compressed sensing with multihypothesis,\u201d 2011 Data Compression Conference, pp.193-202, 2011, 10.1109\/DCC.2011.26. 10.1109\/dcc.2011.26","DOI":"10.1109\/DCC.2011.26"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] J. Chen, Y. Chen, D. Qin, and Y. Kuo, \u201cAn elastic net-based hybrid hypothesis method for compressed video sensing,\u201d Multimedia Tools and Applications, vol.74, no.6, pp.2085-2108, 2015, 10.1007\/s11042-013-1743-y. 10.1007\/s11042-013-1743-y","DOI":"10.1007\/s11042-013-1743-y"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] M. Azghani, M. Karimi, and F. Marvasti, \u201cMultihypothesis compressed video sensing technique,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.26, no.4, pp.627-635, 2016, 10.1109\/TCSVT.2015. 2418586. 10.1109\/tcsvt.2015.2418586","DOI":"10.1109\/TCSVT.2015.2418586"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] C. Chen, D. Zhang, and J. Liu, \u201cResample-based hybrid multi-hypothesis scheme for distributed compressive video sensing,\u201d IEICE Trans. Inf. & Syst., vol.100, no.12, pp.3073-3076, 2017, 10.1587\/ transinf.2017EDL8133. 10.1587\/transinf.2017edl8133","DOI":"10.1587\/transinf.2017EDL8133"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] E. Elhamifar and R. Vidal, \u201cSparse subspace clustering: Algorithm, theory, and applications,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.11, pp.2765-2781, 2013, 10.1109\/TPAMI.2013.57. 10.1109\/tpami.2013.57","DOI":"10.1109\/TPAMI.2013.57"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] Y. Li, W. Dai, J. Zou, H. Xiong, and Y.F. Zheng, \u201cStructured sparse representation with union of data-driven linear and multilinear subspaces model for compressive video sampling,\u201d IEEE Trans. Signal Process., vol.65, no.19, pp.5062-5077, 2017, 10.1109\/TSP.2017.2721905. 10.1109\/tsp.2017.2721905","DOI":"10.1109\/TSP.2017.2721905"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] W.B. Johnson and J. Lindenstrauss, \u201cExtensions of Lipschitz mappings into a Hilbert space,\u201d Contemporary mathematics, vol.26, no.1, pp.189-206, 1984, 10.1090\/conm\/026\/737400. 10.1090\/conm\/026\/737400","DOI":"10.1090\/conm\/026\/737400"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] B.-T. Choi, S.-H. Lee, and S.-J. Ko, \u201cNew frame rate up-conversion using bi-directional motion estimation,\u201d IEEE Trans. Consum. Electron., vol.46, no.3, pp.603-609, 2000, 10.1109\/30.883418. 10.1109\/30.883418","DOI":"10.1109\/30.883418"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] C.A. Metzler, A. Maleki, and R.G. Baraniuk, \u201cFrom denoising to compressed sensing,\u201d IEEE Trans. Inf. Theory, vol.62, no.9, pp.5117-5144, 2016, 10.1109\/TIT.2016.2556683. 10.1109\/TIT.2016.2556683","DOI":"10.1109\/TIT.2016.2556683"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] D. Needell and J.A. Tropp, \u201cCoSaMP: Iterative signal recovery from incomplete and inaccurate samples,\u201d Applied and computational harmonic analysis, vol.26, no.3, pp.301-321, 2009, 10.1016\/j.acha.2008.07.002. 10.1016\/j.acha.2008.07.002","DOI":"10.1016\/j.acha.2008.07.002"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] A. Beck and M. Teboulle, \u201cA fast iterative shrinkage-thresholding algorithm for linear inverse problems,\u201d SIAM journal on imaging sciences, vol.2, no.1, pp.183-202, 2009. 10.1137\/080716542","DOI":"10.1137\/080716542"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] Z. Wen, B. Hou, and L. Jiao, \u201cJoint sparse recovery with semisupervised MUSIC,\u201d IEEE Signal Process. Lett., vol.24, no.5, pp.629-633, 2017, 10.1109\/LSP.2017.2680603. 10.1109\/lsp.2017.2680603","DOI":"10.1109\/LSP.2017.2680603"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] M.F. Duarte, M.B. Wakin, D. Baron, et al., \u201cUniversal distributed sensing via random projections,\u201d Proc. 5th International Conference on Information Processing in Sensor Networks, pp.177-185, 2006.","DOI":"10.1109\/IPSN.2006.244161"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] J. Prades-Nebot, Y. Ma, and T. Huang, \u201cDistributed video coding using compressive sampling,\u201d Picture Coding Symposium, pp.1-4, 2009. 10.1109\/pcs.2009.5167431","DOI":"10.1109\/PCS.2009.5167431"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/11\/E102.D_2019EDP7133\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,2]],"date-time":"2019-11-02T03:31:17Z","timestamp":1572665477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/11\/E102.D_2019EDP7133\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,1]]},"references-count":41,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7133","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,1]]}}}