{"id":"https://openalex.org/W4304014851","doi":"https://doi.org/10.1145/3503161.3547918","title":"Content and Gradient Model-driven Deep Network for Single Image Reflection Removal","display_name":"Content and Gradient Model-driven Deep Network for Single Image Reflection Removal","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304014851","doi":"https://doi.org/10.1145/3503161.3547918"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547918","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-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/A5100415492","display_name":"Yanan Zhang","orcid":"https://orcid.org/0000-0001-6937-0913"},"institutions":[{"id":"https://openalex.org/I4210104064","display_name":"Shenzhen Academy of Robotics","ror":"https://ror.org/01h027j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104064"]},{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ya-Nan Zhang","raw_affiliation_strings":["Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University & Shenzhen Institute of Artificial Intelligence of Robotics of Society, shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University & Shenzhen Institute of Artificial Intelligence of Robotics of Society, shenzhen, China","institution_ids":["https://openalex.org/I4210104064","https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019313200","display_name":"Linlin Shen","orcid":"https://orcid.org/0000-0003-1420-0815"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlin Shen","raw_affiliation_strings":["Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University & Guangdong Key Laboratory of Intelligent Information Processing, shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University & Guangdong Key Laboratory of Intelligent Information Processing, shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074364531","display_name":"Qiufu Li","orcid":"https://orcid.org/0000-0002-8120-6531"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiufu Li","raw_affiliation_strings":["Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100415492"],"corresponding_institution_ids":["https://openalex.org/I180726961","https://openalex.org/I4210104064"],"apc_list":null,"apc_paid":null,"fwci":0.5397,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7509625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"6802","last_page":"6812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement 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"}},"topics":[{"id":"https://openalex.org/T11019","display_name":"Image Enhancement 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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9973999857902527,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9901999831199646,"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/interpretability","display_name":"Interpretability","score":0.8506464958190918},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8008829355239868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7191548347473145},{"id":"https://openalex.org/keywords/reflection","display_name":"Reflection (computer programming)","score":0.6689150929450989},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6554393768310547},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6143901348114014},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5795758366584778},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5708975195884705},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5585094690322876},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45059192180633545},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4427635669708252},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40554967522621155},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39715611934661865}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8506464958190918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8008829355239868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7191548347473145},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.6689150929450989},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6554393768310547},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6143901348114014},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5795758366584778},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5708975195884705},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5585094690322876},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45059192180633545},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4427635669708252},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40554967522621155},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39715611934661865},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547918","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8287676507","display_name":null,"funder_award_id":"91959108 and 62006156","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":45,"referenced_works":["https://openalex.org/W1558369361","https://openalex.org/W1978900400","https://openalex.org/W1980212291","https://openalex.org/W1983897339","https://openalex.org/W1988700013","https://openalex.org/W2099847307","https://openalex.org/W2104166077","https://openalex.org/W2107530646","https://openalex.org/W2117539524","https://openalex.org/W2122122766","https://openalex.org/W2133665775","https://openalex.org/W2135473332","https://openalex.org/W2166313476","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2518628096","https://openalex.org/W2608451532","https://openalex.org/W2613034492","https://openalex.org/W2748199361","https://openalex.org/W2779896560","https://openalex.org/W2784344583","https://openalex.org/W2798966709","https://openalex.org/W2894734721","https://openalex.org/W2895176907","https://openalex.org/W2955193736","https://openalex.org/W2963676366","https://openalex.org/W2963881378","https://openalex.org/W2967060301","https://openalex.org/W2979722847","https://openalex.org/W2982389402","https://openalex.org/W3018622322","https://openalex.org/W3034647178","https://openalex.org/W3035209350","https://openalex.org/W3035250394","https://openalex.org/W3035302306","https://openalex.org/W3035304527","https://openalex.org/W3085109118","https://openalex.org/W3106250896","https://openalex.org/W3115447952","https://openalex.org/W3120024076","https://openalex.org/W3173090543","https://openalex.org/W4214485187","https://openalex.org/W4234552385","https://openalex.org/W4254161494","https://openalex.org/W4285601347"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Single":[0],"image":[1,65,86,93],"reflection":[2,49,66,87,139,158],"removal":[3,88],"(SIRR)":[4],"is":[5,69,103,169,184],"an":[6,106],"extremely":[7],"challenging,":[8],"ill-posed":[9],"problem":[10],"with":[11],"many":[12],"application":[13],"scenarios.":[14],"In":[15,51],"recent":[16],"years,":[17],"massive":[18],"deep":[19,60],"learning-based":[20],"methods":[21,36,174],"have":[22],"been":[23],"proposed":[24],"to":[25,144,157],"remove":[26],"undesirable":[27],"reflections":[28],"from":[29],"a":[30,56,70,83,118,138,146],"single":[31,64,85],"input":[32],"image.":[33],"However,":[34],"these":[35],"lack":[37],"interpretability":[38],"and":[39,58,72,96,115,150,180],"do":[40],"not":[41],"fully":[42],"utilize":[43],"the":[44,77,92,101,111,124,153],"intrinsic":[45],"physical":[46],"structure":[47],"of":[48,126,177],"images.":[50],"this":[52],"paper,":[53],"we":[54,81,136],"propose":[55],"content":[57,94],"gradient-guided":[59],"network":[61,154],"(CGDNet)":[62],"for":[63],"removal,":[67],"which":[68,90],"full-interpretable":[71],"model-driven":[73],"network.":[74],"Firstly,":[75],"using":[76,105],"multi-scale":[78],"convolutional":[79],"dictionary,":[80],"design":[82],"novel":[84],"model,":[89],"combines":[91],"prior":[95,98],"gradient":[97,113],"information.":[99],"Then,":[100],"model":[102],"optimized":[104],"optimization":[107],"algorithm":[108],"based":[109],"on":[110,162],"proximal":[112],"technique":[114],"unfolded":[116],"into":[117,142],"neural":[119],"network,":[120],"i.e.,":[121],"CGDNet.":[122],"All":[123],"parameters":[125],"CGDNet":[127,143,168],"can":[128],"be":[129],"automatically":[130],"learned":[131],"by":[132],"end-to-end":[133],"training.":[134],"Besides,":[135],"introduce":[137],"detection":[140],"module":[141],"obtain":[145],"probabilistic":[147],"confidence":[148],"map":[149],"ensure":[151],"that":[152,167],"pays":[155],"attention":[156],"regions.":[159],"Extensive":[160],"experiments":[161],"four":[163],"benchmark":[164],"datasets":[165],"demonstrate":[166],"more":[170],"efficient":[171],"than":[172],"state-of-the-art":[173],"in":[175],"terms":[176],"both":[178],"subjective":[179],"objective":[181],"evaluations.":[182],"Code":[183],"available":[185],"at":[186],"https://github.com/zynwl/CGDNet.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}