{"id":"https://openalex.org/W2295336607","doi":"https://doi.org/10.1109/icip.2015.7351075","title":"Group-based hyperspectral image denoising using low rank representation","display_name":"Group-based hyperspectral image denoising using low rank representation","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2295336607","doi":"https://doi.org/10.1109/icip.2015.7351075","mag":"2295336607"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7351075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102714623","display_name":"Mengdi Wang","orcid":"https://orcid.org/0000-0002-2258-5674"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengdi Wang","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101579038","display_name":"Jing Yu","orcid":"https://orcid.org/0000-0002-2854-8620"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Yu","raw_affiliation_strings":["College of Computer Science and Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024318974","display_name":"Weidong Sun","orcid":"https://orcid.org/0000-0002-8931-8407"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Sun","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1623","last_page":"1627"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7964656352996826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7169365882873535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.665321946144104},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6608631014823914},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6575540900230408},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6475784182548523},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6016051769256592},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5872523188591003},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.560126006603241},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5117775797843933},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.5044552087783813},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4758293926715851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43903157114982605},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3517078161239624},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09074670076370239}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7964656352996826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7169365882873535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.665321946144104},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6608631014823914},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6575540900230408},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6475784182548523},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6016051769256592},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5872523188591003},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.560126006603241},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5117775797843933},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.5044552087783813},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4758293926715851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43903157114982605},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3517078161239624},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09074670076370239},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2015.7351075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1736339626","https://openalex.org/W1964860887","https://openalex.org/W1969352102","https://openalex.org/W1974438823","https://openalex.org/W1994040806","https://openalex.org/W2039596145","https://openalex.org/W2053419820","https://openalex.org/W2076767154","https://openalex.org/W2125298866","https://openalex.org/W2131628350","https://openalex.org/W2133665775","https://openalex.org/W2160484748","https://openalex.org/W2171520281","https://openalex.org/W6679973066","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"For":[0],"the":[1,25,50,54,57,61,72,80,85,95,107,110,121],"hyperspectral":[2],"image":[3],"(HSI)":[4],"denoising":[5,108],"problem,":[6],"we":[7],"propose":[8],"a":[9,31,38,64],"group-based":[10],"low":[11,41],"rank":[12,42],"representation":[13,43],"(GLRR)":[14],"method.":[15],"A":[16],"corrupted":[17,81],"HSI":[18],"is":[19,35,104,128],"divided":[20],"into":[21,30],"overlapping":[22],"patches":[23,27,62,70,97],"and":[24,56,115,132],"similar":[26,69],"are":[28,118],"combined":[29],"group.":[32,112],"The":[33,123],"group":[34,65,73],"de-noised":[36],"as":[37,92],"whole":[39],"using":[40],"(LRR).":[44],"Our":[45],"method":[46,127],"can":[47,74],"employ":[48],"both":[49,130],"local":[51],"similarity":[52,59],"within":[53,63,71],"patch":[55,111],"nonlocal":[58,68],"across":[60],"simultaneously,":[66],"while":[67],"bring":[75],"extra":[76],"structure":[77],"information":[78],"for":[79,106],"patch,":[82],"which":[83],"makes":[84],"noise":[86],"more":[87],"significant":[88],"to":[89],"be":[90],"detected":[91],"outliers.":[93],"Since":[94],"uncorrupted":[96],"have":[98],"an":[99],"intrinsic":[100],"low-rank":[101],"structure,":[102],"LRR":[103],"employed":[105],"of":[109,125],"Both":[113],"simulated":[114],"real":[116],"data":[117],"used":[119],"in":[120],"experiments.":[122],"effectiveness":[124],"our":[126],"proved":[129],"qualitatively":[131],"quantitatively.":[133]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}