{"id":"https://openalex.org/W7124693335","doi":"https://doi.org/10.1145/3788690","title":"Unfolding Convolutional Sparse Coding With Low-rank-Guided Hybrid Priors for Image Denoising","display_name":"Unfolding Convolutional Sparse Coding With Low-rank-Guided Hybrid Priors for Image Denoising","publication_year":2026,"publication_date":"2026-01-19","ids":{"openalex":"https://openalex.org/W7124693335","doi":"https://doi.org/10.1145/3788690"},"language":"en","primary_location":{"id":"doi:10.1145/3788690","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3788690","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"},"type":"article","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/A5123341684","display_name":"Yifan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Zhao","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-2305-8067","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073652872","display_name":"Ziyang Zheng","orcid":"https://orcid.org/0000-0001-9923-8016"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyang Zheng","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-9923-8016","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086442836","display_name":"Duoduo Xue","orcid":"https://orcid.org/0000-0002-6608-5700"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duoduo Xue","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6608-5700","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123339646","display_name":"Yong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Shanghai Aerospace Control Technology Institute, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-7551-1137","affiliations":[{"raw_affiliation_string":"Shanghai Aerospace Control Technology Institute, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045342512","display_name":"Wenrui Dai","orcid":"https://orcid.org/0000-0003-2522-5778"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenrui Dai","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2522-5778","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123328355","display_name":"Chenglin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglin Li","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2888-594X","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121765747","display_name":"Junni Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junni Zou","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9694-9880","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002494284","display_name":"Hongkai Xiong","orcid":"https://orcid.org/0000-0003-4552-0029"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongkai Xiong","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4552-0029","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.8969,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94803055,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"22","issue":"3","first_page":"1","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.8891000151634216,"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":0.8891000151634216,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.040800001472234726,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0215000007301569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6614000201225281},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6226000189781189},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5723999738693237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.44359999895095825},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4237000048160553},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41190001368522644},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4009000062942505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8533999919891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6967999935150146},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6614000201225281},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6226000189781189},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5723999738693237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4237000048160553},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.3361999988555908},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.28999999165534973},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2768999934196472},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2500999867916107},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3788690","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3788690","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2209139840","display_name":null,"funder_award_id":"62401357, 62201339, 62371288, and 62320106003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2091449379","https://openalex.org/W2103972604","https://openalex.org/W2160547390","https://openalex.org/W2161991491","https://openalex.org/W2508457857","https://openalex.org/W2764207251","https://openalex.org/W2776654051","https://openalex.org/W2792944346","https://openalex.org/W2952071070","https://openalex.org/W2963725279","https://openalex.org/W2971719842","https://openalex.org/W2974585710","https://openalex.org/W2995679912","https://openalex.org/W3085462114","https://openalex.org/W3097646105","https://openalex.org/W3099686304","https://openalex.org/W3119741437","https://openalex.org/W3140079845","https://openalex.org/W3167568784","https://openalex.org/W3175028147","https://openalex.org/W3214753474","https://openalex.org/W4205865775","https://openalex.org/W4210896693","https://openalex.org/W4283310858","https://openalex.org/W4294068732","https://openalex.org/W4312812783","https://openalex.org/W4313427334","https://openalex.org/W4388820247","https://openalex.org/W4389459196","https://openalex.org/W4390489067","https://openalex.org/W4401328210","https://openalex.org/W4402350365","https://openalex.org/W4407168967","https://openalex.org/W4411333052"],"related_works":[],"abstract_inverted_index":{"Image":[0],"denoising":[1,77,217],"remains":[2],"a":[3,21,121,185],"challenging":[4],"problem":[5],"due":[6],"to":[7,137,157,168,190],"the":[8,51,67,94,106,127,162,166,220,223],"ill-posed":[9],"nature":[10],"of":[11,53,87,145,222],"recovering":[12],"clean":[13],"images":[14],"from":[15,161],"noisy":[16],"observations.":[17],"Prior-based":[18],"modeling":[19,35],"plays":[20],"vital":[22],"role":[23],"in":[24,50],"addressing":[25],"this":[26],"challenge.":[27],"Low-rank":[28],"priors":[29,119],"effectively":[30],"exploit":[31],"non-local":[32],"self-similarity":[33],"by":[34,79],"correlations":[36],"across":[37,69],"similar":[38],"images,":[39],"but":[40,85],"often":[41],"over-smooth":[42],"fine":[43],"textures.":[44],"In":[45],"contrast,":[46],"sparse":[47,116,146],"priors,":[48,164],"especially":[49],"form":[52],"Convolutional":[54],"Sparse":[55],"Coding":[56],"(CSC),":[57],"excel":[58],"at":[59],"preserving":[60],"high-frequency":[61],"texture":[62,171],"details,":[63],"yet":[64],"typically":[65],"neglect":[66],"structure":[68,97],"images.":[70],"Recent":[71],"unfolded":[72,109],"CSC":[73,81,110],"networks":[74],"have":[75],"improved":[76],"performance":[78,178,205],"combining":[80],"with":[82,206],"deep":[83,231],"networks,":[84],"most":[86],"them":[88],"rely":[89],"solely":[90],"on":[91,179,212],"sparsity,":[92],"overlooking":[93],"complementary":[95],"low-rank":[96,118,149],"information.":[98],"To":[99,175],"overcome":[100],"these":[101],"limitations,":[102],"we":[103],"propose":[104],"UCSC-LR,":[105],"first":[107],"interpretable":[108],"network":[111],"that":[112],"jointly":[113],"leverages":[114],"both":[115,126,170],"and":[117,132,148,172,199,215,230],"within":[120],"unified":[122],"architecture.":[123],"UCSC-LR":[124,183,202],"unrolls":[125],"Iterative":[128],"Shrinkage-Thresholding":[129],"Algorithm":[130],"(ISTA)":[131],"Singular":[133],"Value":[134],"Thresholding":[135],"(SVT)":[136],"strictly":[138],"implement":[139],"alternating":[140],"minimization,":[141],"enabling":[142],"simultaneous":[143],"pursuit":[144],"representations":[147],"reconstructions.":[150],"A":[151],"dedicated":[152],"Fusion":[153],"Net":[154],"is":[155],"introduced":[156],"adaptively":[158],"integrate":[159],"features":[160],"two":[163],"allowing":[165],"model":[167,208],"preserve":[169],"structural":[173],"content.":[174],"further":[176],"enhance":[177],"color":[180,216],"image":[181],"denoising,":[182],"incorporates":[184],"lightweight":[186],"channel":[187],"attention":[188],"mechanism":[189],"capture":[191],"inter-channel":[192],"dependencies.":[193],"With":[194],"shared,":[195],"pre-learned":[196],"convolutional":[197],"dictionaries":[198],"efficient":[200],"parameterization,":[201],"achieves":[203],"state-of-the-art":[204],"low":[207],"complexity.":[209],"Extensive":[210],"experiments":[211],"grayscale,":[213],"blind,":[214],"tasks":[218],"validate":[219],"effectiveness":[221],"proposed":[224],"method,":[225],"consistently":[226],"surpassing":[227],"existing":[228],"CSC-based":[229],"learning-based":[232],"denoisers.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-01-20T00:00:00"}