{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:04:59Z","timestamp":1730246699478,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1109\/icip.2019.8803239","type":"proceedings-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T19:32:48Z","timestamp":1566847968000},"page":"3128-3132","source":"Crossref","is-referenced-by-count":6,"title":["Mixed Noise Removal for Hyperspectral Images Using Hybrid Spatio-Spectral Total Variation"],"prefix":"10.1109","author":[{"given":"Saori","family":"Takeyama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsuke","family":"Ono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itsuo","family":"Kumazawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1016\/0898-1221(76)90003-1"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1007\/BF01581204"},{"key":"ref12","first-page":"1","article-title":"Distributed optimization and statistical learning via the alternating direction method of multiplie","volume":"3","author":"boyd","year":"2011"},{"key":"ref13","first-page":"2897","article-title":"Fonctions convexes duales et points proximaux dans un espace hilberti","volume":"255","author":"moreau","year":"1962","journal-title":"C R Acad Sci Paris Ser A Math"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1145\/1970392.1970395"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/TIP.2010.2076294"},{"key":"ref16","first-page":"1","article-title":"Epigraphical projection and proximal tools for solving constrained convex optimization proble","author":"chierchia","year":"2014","journal-title":"Signal Image and Video Process"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/TSP.2015.2472365"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/TIP.2017.2651392"},{"key":"ref19","first-page":"1603","article-title":"Robust and effective hy-perspectral pansharpening using spatio-spectral total variati","author":"takeyama","year":"2018","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP"},{"key":"ref4","first-page":"2733","article-title":"Non-convex low-rank approximation for hyperspectral image recovery with weighted total varaition regularizati","author":"li","year":"2018","journal-title":"Proc IEEE Int Geosci Remote Sens Symp (IGARSS)"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TGRS.2015.2452812","article-title":"Total-variation-regularized low-rank matrix factorization for hyperspectral image restorati","volume":"54","author":"he","year":"2016","journal-title":"Proc IEEE Int Geosci Remote Sens Symp (IGARSS)"},{"key":"ref6","first-page":"2411","article-title":"Robust principal component analysis with matrix factorizati","author":"chen","year":"2018","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/TGRS.2016.2547879"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.3934\/ipi.2008.2.455"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/TGRS.2012.2185054"},{"key":"ref2","first-page":"110s","article-title":"Recent advances in techniques for hyperspectral image processi","volume":"113","author":"plaza","year":"2009"},{"key":"ref1","volume":"1","author":"chang","year":"2003","journal-title":"Hyperspectral Imaging Techniques for Spectral Detection and Classification"},{"key":"ref9","first-page":"4586","article-title":"Hyperspectral image restoration by hybrid spatio-spectral total variati","author":"takeyama","year":"2017","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP"},{"key":"ref20","first-page":"272","author":"duchi","year":"2008","journal-title":"Proc 22nd Int Conf Mach Learn (ICML)"},{"year":"0","article-title":"Spect","key":"ref22"},{"key":"ref21","first-page":"442","article-title":"Hyperspectral image denoising using spatio-spectral total variati","volume":"13","author":"aggarwal","year":"2016","journal-title":"IEEE Geosci and Remote Sens Lett"},{"year":"0","article-title":"G","key":"ref24"},{"year":"0","article-title":"Multisp","key":"ref23"}],"event":{"name":"2019 IEEE International Conference on Image Processing (ICIP)","start":{"date-parts":[[2019,9,22]]},"location":"Taipei, Taiwan","end":{"date-parts":[[2019,9,25]]}},"container-title":["2019 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8791230\/8799366\/08803239.pdf?arnumber=8803239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T15:19:58Z","timestamp":1658157598000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8803239\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/icip.2019.8803239","relation":{},"subject":[],"published":{"date-parts":[[2019,9]]}}}