{"id":"https://openalex.org/W4225817345","doi":"https://doi.org/10.1109/tcsvt.2022.3170689","title":"Deep Sparse Representation Based Image Restoration With Denoising Prior","display_name":"Deep Sparse Representation Based Image Restoration With Denoising Prior","publication_year":2022,"publication_date":"2022-04-26","ids":{"openalex":"https://openalex.org/W4225817345","doi":"https://doi.org/10.1109/tcsvt.2022.3170689"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2022.3170689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3170689","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","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/A5083556808","display_name":"Wei Xu","orcid":"https://orcid.org/0000-0002-1670-5174"},"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":true,"raw_author_name":"Wei Xu","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1670-5174","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101944238","display_name":"Qing Zhu","orcid":"https://orcid.org/0000-0002-6146-9190"},"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":"Qing Zhu","raw_affiliation_strings":["Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6146-9190","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081279369","display_name":"Na Qi","orcid":"https://orcid.org/0000-0003-3778-7305"},"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":"Na Qi","raw_affiliation_strings":["Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3778-7305","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039567778","display_name":"Dongpan Chen","orcid":"https://orcid.org/0000-0003-2011-100X"},"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":"Dongpan Chen","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2011-100X","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083556808"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":3.7755,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.94612518,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"32","issue":"10","first_page":"6530","last_page":"6542"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9997000098228455,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.9197001457214355},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.792945384979248},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7922627925872803},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7716113328933716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7317290902137756},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6578795909881592},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6312729120254517},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5689646005630493},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.5186468958854675},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47485899925231934},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.4402751326560974},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.43920397758483887},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4259242117404938},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.27280372381210327},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24256780743598938}],"concepts":[{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.9197001457214355},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.792945384979248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7922627925872803},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7716113328933716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7317290902137756},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6578795909881592},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6312729120254517},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5689646005630493},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.5186468958854675},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47485899925231934},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.4402751326560974},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.43920397758483887},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4259242117404938},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27280372381210327},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24256780743598938},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2022.3170689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3170689","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G3058693870","display_name":null,"funder_award_id":"61906009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8871854399","display_name":null,"funder_award_id":"KM202010005018","funder_id":"https://openalex.org/F4320321572","funder_display_name":"Beijing Municipal Commission of Education"},{"id":"https://openalex.org/G935306776","display_name":null,"funder_award_id":"2021B06","funder_id":"https://openalex.org/F4320321913","funder_display_name":"Beijing University of Technology"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321572","display_name":"Beijing Municipal Commission of Education","ror":"https://ror.org/04bpn6s66"},{"id":"https://openalex.org/F4320321913","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W935139217","https://openalex.org/W1901129140","https://openalex.org/W1906770428","https://openalex.org/W1930824406","https://openalex.org/W1978749115","https://openalex.org/W1993205988","https://openalex.org/W1994281301","https://openalex.org/W1995228944","https://openalex.org/W2011181254","https://openalex.org/W2042984553","https://openalex.org/W2044810215","https://openalex.org/W2045737896","https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2065277358","https://openalex.org/W2078187167","https://openalex.org/W2078394884","https://openalex.org/W2087416986","https://openalex.org/W2097323375","https://openalex.org/W2115706991","https://openalex.org/W2117259536","https://openalex.org/W2117865218","https://openalex.org/W2121927366","https://openalex.org/W2131686571","https://openalex.org/W2138204001","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2161516371","https://openalex.org/W2172275395","https://openalex.org/W2194775991","https://openalex.org/W2202656999","https://openalex.org/W2242218935","https://openalex.org/W2338117771","https://openalex.org/W2508457857","https://openalex.org/W2512704900","https://openalex.org/W2532801510","https://openalex.org/W2613155248","https://openalex.org/W2729550389","https://openalex.org/W2747898905","https://openalex.org/W2764207251","https://openalex.org/W2774320778","https://openalex.org/W2781681626","https://openalex.org/W2784344583","https://openalex.org/W2792275277","https://openalex.org/W2798427787","https://openalex.org/W2866634454","https://openalex.org/W2898685680","https://openalex.org/W2938629629","https://openalex.org/W2940742415","https://openalex.org/W2949128855","https://openalex.org/W2952071070","https://openalex.org/W2963814976","https://openalex.org/W2964125708","https://openalex.org/W3000775737","https://openalex.org/W3016440669","https://openalex.org/W3035302306","https://openalex.org/W3041324976","https://openalex.org/W3099737327","https://openalex.org/W3102025760","https://openalex.org/W3105776658","https://openalex.org/W3137074406","https://openalex.org/W3167568784","https://openalex.org/W3175028147","https://openalex.org/W3185385692","https://openalex.org/W3193659426","https://openalex.org/W3194730817","https://openalex.org/W4250297470","https://openalex.org/W4290044043","https://openalex.org/W6631190155","https://openalex.org/W6752237900","https://openalex.org/W6762492139","https://openalex.org/W6767423860"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W2362527261","https://openalex.org/W2606416966","https://openalex.org/W2034957211","https://openalex.org/W3135409736","https://openalex.org/W2031622293","https://openalex.org/W1509360352","https://openalex.org/W2073489937","https://openalex.org/W2613547749","https://openalex.org/W2353554252"],"abstract_inverted_index":{"As":[0],"a":[1,75,101,111],"powerful":[2],"statistical":[3],"signal":[4],"modeling":[5],"technique,":[6],"sparse":[7,58,93,102],"representation":[8,59,94],"has":[9,36],"been":[10,37],"widely":[11],"used":[12],"in":[13,26,32,128],"various":[14],"image":[15,121,146],"restoration":[16],"(IR)":[17],"applications.":[18],"The":[19,64],"sparsity-based":[20],"methods":[21],"have":[22],"achieved":[23],"leading":[24],"performance":[25,160],"the":[27,43,54,89,92,107,120,126,153],"past":[28],"few":[29],"decades.":[30],"However,":[31],"recent":[33,44],"years":[34],"it":[35,73],"surpassed":[38],"by":[39],"other":[40],"methods,":[41],"especially":[42],"deep":[45,76,85,155],"learning":[46],"based":[47,95],"methods.":[48,166],"In":[49,97],"this":[50,68],"paper,":[51],"we":[52,66,81,99],"address":[53],"question":[55,69],"that":[56,87,152],"whether":[57],"can":[60,134,157],"be":[61,79,135],"competitive":[62],"again.":[63],"way":[65],"answer":[67],"is":[70],"to":[71,105,118],"redesign":[72],"with":[74],"architecture.":[77],"To":[78],"specific,":[80],"propose":[82],"an":[83],"end-to-end":[84,124],"architecture":[86],"follows":[88],"process":[90],"of":[91],"IR.":[96],"particular,":[98],"learn":[100],"convolutional":[103,112,129],"dictionary":[104,130],"replace":[106,119],"traditional":[108],"dictionary,":[109],"and":[110,131,149,164],"neural":[113],"network":[114,156],"(CNN)":[115],"denoising":[116],"prior":[117],"prior.":[122],"Through":[123],"training,":[125],"parameters":[127],"CNN":[132],"denoiser":[133],"jointly":[136],"optimized.":[137],"Experimental":[138],"results":[139],"on":[140],"several":[141],"representative":[142],"IR":[143],"tasks,":[144],"including":[145],"denoising,":[147],"deblurring":[148],"super-resolution,":[150],"demonstrate":[151],"proposed":[154],"achieve":[158],"superior":[159],"against":[161],"state-of-the-art":[162],"model-based":[163],"learning-based":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
