{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T04:05:41Z","timestamp":1750910741378,"version":"3.41.0"},"reference-count":54,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2017]]},"DOI":"10.1587\/transinf.2016pcp0003","type":"journal-article","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T22:27:19Z","timestamp":1504218439000},"page":"1953-1961","source":"Crossref","is-referenced-by-count":1,"title":["Image Restoration with Multiple Hard Constraints on Data-Fidelity to Blurred\/Noisy Image Pair"],"prefix":"10.1587","volume":"E100.D","author":[{"given":"Saori","family":"TAKEYAMA","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering at the Tokyo Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsuke","family":"ONO","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research (IIR), Tokyo Institute of Technology"},{"name":"Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itsuo","family":"KUMAZAWA","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research (IIR), Tokyo Institute of Technology"},{"name":"Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] Q. Shan, J. Jia, and A. Agarwala, \u201cHigh-quality motion deblurring from a single image,\u201d ACM Trans. Graph., vol.27, no.3, pp.73:1-73:10, 2008. 10.1145\/1360612.1360672","DOI":"10.1145\/1360612.1360672"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] S. Cho and S. Lee, \u201cFast motion deblurring,\u201d ACM Trans. Graph., vol.28, no.5, pp.145:1-145:8, 2009. 10.1145\/1618452.1618491","DOI":"10.1145\/1618452.1618491"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J.-F. Cai, H. Ji, C. Liu, and Z. Shen, \u201cBlind motion deblurring from a single image using sparse approximation,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.104-111, 2009. 10.1109\/cvprw.2009.5206743","DOI":"10.1109\/CVPR.2009.5206743"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] L. Xu and J. Jia, \u201cTwo-phase kernel estimation for robust motion deblurring,\u201d Proc. Eur. Conf. Comput. Vis. (ECCV), vol.6311, pp.157-170, 2010. 10.1007\/978-3-642-15549-9_12","DOI":"10.1007\/978-3-642-15549-9_12"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] D. Krishnan, T. Tay, and R. Fergus, \u201cBlind deconvolution using a normalized sparsity measure,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.233-240, 2011. 10.1109\/cvpr.2011.5995521","DOI":"10.1109\/CVPR.2011.5995521"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J.-F. Cai, H. Ji, C. Liu, and Z. Shen, \u201cFramelet-based blind motion deblurring from a single image,\u201d IEEE Trans. Image Process., vol.21, no.2, pp.562-572, 2012. 10.1109\/tip.2011.2164413","DOI":"10.1109\/TIP.2011.2164413"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] L. Zhong, S. Cho, D. Metaxas, S. Paris, and J. Wang, \u201cHandling noise in single image deblurring using directional filters,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.612-619, 2013. 10.1109\/cvpr.2013.85","DOI":"10.1109\/CVPR.2013.85"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] T. Michaeli and M. Irani, \u201cBlind deblurring using internal patch recurrence,\u201d Proc. Eur. Conf. Comput. Vis. (ECCV), vol.8691, pp.783-798, 2014. 10.1007\/978-3-319-10578-9_51","DOI":"10.1007\/978-3-319-10578-9_51"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] D. Perrone and P. Favaro, \u201cA clearer picture of total variation blind deconvolution,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.6, pp.1041-1055, 2016. 10.1109\/tpami.2015.2477819","DOI":"10.1109\/TPAMI.2015.2477819"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] W.-S. Lai, J.-B. Huang, Z. Hu, N. Ahuja, and M.-H. Yang, \u201cA comparative study for single image blind deblurring,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.1701-1709, 2016. 10.1109\/cvpr.2016.188","DOI":"10.1109\/CVPR.2016.188"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, \u201cImage deblurring with blurred\/noisy image pairs,\u201d ACM Trans. Graph., vol.26, no.3, pp.1:1-1:10, 2007. 10.1145\/1239451.1239452","DOI":"10.1145\/1276377.1276379"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S.-H. Lee, H.-M. Park, and S.-Y. Hwang, \u201cMotion deblurring using edge map with blurred\/noisy image pairs,\u201d Optics Communications, vol.285, no.7, pp.1777-1786, 2012. 10.1016\/j.optcom.2011.12.057","DOI":"10.1016\/j.optcom.2011.12.057"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] C. Je, H.S. Jeon, C.-H. Son, and H.-M. Park, \u201cDisparity-based space-variant image deblurring,\u201d Signal Process.: Image Commun., vol.28, no.7, pp.792-808, 2013. 10.1016\/j.image.2013.04.005","DOI":"10.1016\/j.image.2013.04.005"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] H. Li, Y. Zhang, J. Sun, and D. Gong, \u201cJoint motion deblurring with blurred\/noisy image pair,\u201d Proc. Int. Conf. Pattern Recognit. (ICPR), pp.1020-1024, 2014. 10.1109\/icpr.2014.185","DOI":"10.1109\/ICPR.2014.185"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C.-H. Son and X.-P. Zhang, \u201cLayer-based approach for image pair fusion,\u201d IEEE Trans. Image Process., vol.25, no.6, pp.2866-2881, 2016. 10.1109\/tip.2016.2556618","DOI":"10.1109\/TIP.2016.2556618"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] D. Gabay and B. Mercier, \u201cA dual algorithm for the solution of nonlinear variational problems via finite elements approximations,\u201d Comput. Math. Appl., vol.2, pp.17-40, 1976. 10.1016\/0898-1221(76)90003-1","DOI":"10.1016\/0898-1221(76)90003-1"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Eckstein and D.P. Bertsekas, \u201cOn the Douglas \u2014 Rachford splitting method and the proximal point algorithm for maximal monotone operators,\u201d Math. Program., vol.55, no.1-3, pp.293-318, 1992. 10.1007\/bf01581204","DOI":"10.1007\/BF01581204"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, \u201cDistributed optimization and statistical learning via the alternating direction method of multipliers,\u201d Foundations and Trends in Machine Learning, vol.3, no.1, pp.1-122, 2011. 10.1561\/2200000016","DOI":"10.1561\/2200000016"},{"key":"19","unstructured":"[19] J.J. Moreau, \u201cFonctions convexes duales et points proximaux dans un espace hilbertien,\u201d C. R. Acad. Sci. Paris Ser. A Math., vol.255, pp.2897-2899, 1962."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] H.H. Bauschke and P.L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces, Springer, New York, 2011.","DOI":"10.1007\/978-1-4419-9467-7"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] L. Zhong, S. Cho, D. Metaxas, S. Paris, and J. Wang, \u201cHandling noise in single image deblurring using directional filters,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.612-619, 2013. 10.1109\/cvpr.2013.85","DOI":"10.1109\/CVPR.2013.85"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] L.I. Rudin, S. Osher, and E. Fatemi, \u201cNonlinear total variation based noise removal algorithms,\u201d Phys. D, vol.60, no.1-4, pp.259-268, 1992. 10.1016\/0167-2789(92)90242-f","DOI":"10.1016\/0167-2789(92)90242-F"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] X. Bresson and T.F. Chan, \u201cFast dual minimization of the vectorial total variation norm and applications to color image processing,\u201d Inverse Probl. Imag., vol.2, no.4, pp.455-484, 2008. 10.3934\/ipi.2008.2.455","DOI":"10.3934\/ipi.2008.2.455"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] B. Goldluecke, E. Strekalovskiy, and D. Cremers, \u201cThe natural vectorial total variation which arises from geometric measure theory,\u201d SIAM J. Imag. Sci., vol.5, no.2, pp.537-563, 2012. 10.1137\/110823766","DOI":"10.1137\/110823766"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] S. Ono and I. Yamada, \u201cDecorrelated vectorial total variation,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.4090-4097, 2014. 10.1109\/cvpr.2014.521","DOI":"10.1109\/CVPR.2014.521"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] S. Lefkimmiatis, A. Roussos, P. Maragos, and M. Unser, \u201cStructure tensor total variation,\u201d SIAM J. Imag. Sci., vol.8, no.2, pp.1090-1122, 2015. 10.1137\/14098154x","DOI":"10.1137\/14098154X"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] S. Ono, K. Shirai, and M. Okuda, \u201cVectorial total variation based on arranged structure tensor for multichannel image restoration,\u201d Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.4528-4532, 2016. 10.1109\/icassp.2016.7472534","DOI":"10.1109\/ICASSP.2016.7472534"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] P.L. Combettes and J.-C. Pesquet, \u201cProximal splitting methods in signal processing,\u201d Fixed-Point Algorithms for Inverse Problems in Science and Engineering, vol.49, pp.185-212, Springer-Verlag, 2011. 10.1007\/978-1-4419-9569-8_10","DOI":"10.1007\/978-1-4419-9569-8_10"},{"key":"29","unstructured":"[29] S. Ono, \u201cDistributed convex optimization via proximal splitting: A survey on admm-based approaches,\u201d Journal of The Society of Instrument and Control Engineers, vol.55, no.11, pp.954-959, 2016. (in Japanese)."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] S. Mallat, A wavelet tour of signal processing, 2nd ed., Academic Press, 1999.","DOI":"10.1016\/B978-012466606-1\/50008-8"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] E. Cand\u00e8s, L. Demanet, D. Donoho, and L. Ying, \u201cFast discrete curvelet transforms,\u201d SIAM J. Multi. Model. Simul., vol.5, no.3, pp.861-899, 2006. 10.1137\/05064182x","DOI":"10.1137\/05064182X"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] G. Gilboa and S. Osher, \u201cNonlocal linear image regularization and supervised segmentation,\u201d Multiscale Model. Simul., vol.6, no.2, pp.595-630, 2007. 10.1137\/060669358","DOI":"10.1137\/060669358"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] A. Danielyan, V. Katkovnik, and K. Egiazarian, \u201cBM3D frames and variational image deblurring,\u201d IEEE Trans. Image Process., vol.21, no.4, pp.1715-1728, 2012. 10.1109\/tip.2011.2176954","DOI":"10.1109\/TIP.2011.2176954"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] G. Chierchia, N. Pustelnik, B. Pesquet-Popescu, and J.-C. Pesquet, \u201cA nonlocal structure tensor-based approach for multicomponent image recovery problems,\u201d IEEE Trans. Image Process., vol.23, no.12, pp.5531-5544, 2014. 10.1109\/tip.2014.2364141","DOI":"10.1109\/TIP.2014.2364141"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, \u201cNon-local sparse models for image restoration,\u201d Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp.2272-2279, 2009. 10.1109\/iccv.2009.5459452","DOI":"10.1109\/ICCV.2009.5459452"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] D. Zoran and Y. Weiss, \u201cFrom learning models of natural image patches to whole image restoration,\u201d Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp.479-486, 2011. 10.1109\/iccv.2011.6126278","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] S. Sreehari, S.V. Venkatakrishnan, B. Wohlberg, G.T. Buzzard, L.F. Drummy, J.P. Simmons, and C.A. Bouman, \u201cPlug-and-play priors for bright field electron tomography and sparse interpolation,\u201d IEEE Trans. Comput. Imag., vol.2, no.4, pp.408-423, 2016. 10.1109\/tci.2016.2599778","DOI":"10.1109\/TCI.2016.2599778"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] S. Ono, \u201cPrimal-dual plug-and-play image restoration,\u201d IEEE Signal Process. Lett., vol.24, no.8, pp.1108-1112, 2017. 10.1109\/lsp.2017.2710233","DOI":"10.1109\/LSP.2017.2710233"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] M.V. Afonso, J.M. Bioucas-Dias, and M.A.T. Figueiredo, \u201cAn augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems,\u201d IEEE Trans. Image Process., vol.20, no.3, pp.681-695, 2011. 10.1109\/tip.2010.2076294","DOI":"10.1109\/TIP.2010.2076294"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] M. Carlavan and L. Blanc-Feraud, \u201cSparse Poisson noisy image deblurring,\u201d IEEE Trans. Image Process., vol.21, no.4, pp.1834-1846, 2012. 10.1109\/tip.2011.2175934","DOI":"10.1109\/TIP.2011.2175934"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] T. Teuber, G. Steidl, and R.H. Chan, \u201cMinimization and parameter estimation for seminorm regularization models with <i>I<\/i>-divergence constraints,\u201d Inverse Problems, vol.29, no.3, p.035007, 2013. 10.1088\/0266-5611\/29\/3\/035007","DOI":"10.1088\/0266-5611\/29\/3\/035007"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] G. Chierchia, N. Pustelnik, J.-C. Pesquet, and B. Pesquet-Popescu, \u201cEpigraphical projection and proximal tools for solving constrained convex optimization problems,\u201d Signal, Image and Video Process., vol.9, no.8, pp.1737-1749, 2015. 10.1007\/s11760-014-0664-1","DOI":"10.1007\/s11760-014-0664-1"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] S. Ono and I. Yamada, \u201cSignal recovery with certain involved convex data-fidelity constraints,\u201d IEEE Trans. Signal Process., vol.63, no.22, pp.6149-6163, 2015. 10.1109\/tsp.2015.2472365","DOI":"10.1109\/TSP.2015.2472365"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] S. Ono, \u201c<i>L<\/i><sub>0<\/sub> gradient projection,\u201d IEEE Trans. Image Process., vol.26, no.4, pp.1554-1564, 2017. 10.1109\/tip.2017.2651392","DOI":"10.1109\/TIP.2017.2651392"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] S. Takeyama, S. Ono, and I. Kumazawa, \u201cHyperspectral image restoration by hybrid spatio-spectral total variation,\u201d Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.4586-4590, 2017. 10.1109\/icassp.2017.7953025","DOI":"10.1109\/ICASSP.2017.7953025"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] P.C. Hansen, J.G. Nagy, and D.P. O&apos;Leary, Deblurring Images: Matrices, Spectra, and Filtering, SIAM, 2006.","DOI":"10.1137\/1.9780898718874"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] J. Kovacevic and A. Chebira, \u201cLife beyond bases: The advent of frames (Part I),\u201d IEEE Signal Process. Magazine, vol.24, no.4, pp.86-104, 2007. 10.1109\/msp.2007.4286567","DOI":"10.1109\/MSP.2007.4286567"},{"key":"48","unstructured":"[48] G.H. Golub and C.F.V. Loan, Matrix Computations, 4th ed., Johns Hopkins University Press, 2012."},{"key":"49","doi-asserted-by":"crossref","unstructured":"[49] D. Martin, C. Fowlkes, D. Tal, and J. Malik, \u201cA database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,\u201d Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp.416-423, 2001. 10.1109\/iccv.2001.937655","DOI":"10.1109\/ICCV.2001.937655"},{"key":"50","doi-asserted-by":"publisher","unstructured":"[50] J.-F. Aujol, G. Gilboa, T. Chan, and S. Osher, \u201cStructure-texture image decomposition \u2014 modeling, algorithms, and parameter selection,\u201d Int. J. Comput. Vis., vol.67, no.1, pp.111-136, 2006. 10.1007\/s11263-006-4331-z","DOI":"10.1007\/s11263-006-4331-z"},{"key":"51","doi-asserted-by":"crossref","unstructured":"[51] V. Duval, J.-F. Aujol, and L.A. Vese, \u201cMathematical modeling of textures: application to color image decomposition with a projected gradient algorithm,\u201d J. Math. Imag. Vis., vol.37, no.3, pp.232-248, 2010. 10.1007\/s10851-010-0203-9","DOI":"10.1007\/s10851-010-0203-9"},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] S. Ono, T. Miyata, I. Yamada, and K. Yamaoka, \u201cImage recovery by decomposition with component-wise regularization,\u201d IEICE Trans. Fundamentals., vol.E95-A, no.12, pp.2470-2478, 2012. 10.1587\/transfun.e95.a.2470","DOI":"10.1587\/transfun.E95.A.2470"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[53] S. Ono, T. Miyata, and I. Yamada, \u201cCartoon-texture image decomposition using blockwise low-rank texture characterization,\u201d IEEE Trans. Image Process., vol.23, no.3, pp.1128-1142, 2014. 10.1109\/tip.2014.2299067","DOI":"10.1109\/TIP.2014.2299067"},{"key":"54","doi-asserted-by":"crossref","unstructured":"[54] S. Ono, M. Yamagishi, T. Miyata, and I. Kumazawa, \u201cImage restoration using a stochastic variant of the alternating direction method of multipliers,\u201d Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.4523-4527, 2016. 10.1109\/icassp.2016.7472533","DOI":"10.1109\/ICASSP.2016.7472533"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/9\/E100.D_2016PCP0003\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T12:24:04Z","timestamp":1750854244000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/9\/E100.D_2016PCP0003\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":54,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2016pcp0003","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2017]]}}}