{"id":"https://openalex.org/W2887181327","doi":"https://doi.org/10.1145/3240508.3240636","title":"Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining","display_name":"Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining","publication_year":2018,"publication_date":"2018-10-15","ids":{"openalex":"https://openalex.org/W2887181327","doi":"https://doi.org/10.1145/3240508.3240636","mag":"2887181327"},"language":"en","primary_location":{"id":"doi:10.1145/3240508.3240636","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th 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/A5042965510","display_name":"Guanbin Li","orcid":"https://orcid.org/0000-0002-4805-0926"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanbin Li","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603785","display_name":"Xiang He","orcid":"https://orcid.org/0000-0003-1158-5633"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang He","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100688685","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0002-0761-5871"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019953360","display_name":"Huiyou Chang","orcid":"https://orcid.org/0000-0002-4595-2625"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiyou Chang","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101960393","display_name":"Le Dong","orcid":"https://orcid.org/0000-0002-4198-5702"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Dong","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412937","display_name":"Liang Lin","orcid":"https://orcid.org/0000-0003-2248-3755"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Lin","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.5225,"has_fulltext":false,"cited_by_count":254,"citation_normalized_percentile":{"value":0.99122916,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1056","last_page":"1064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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":1.0,"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":0.9983999729156494,"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":0.9980999827384949,"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/computer-science","display_name":"Computer science","score":0.7910346984863281},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6808748841285706},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6782422661781311},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6280267238616943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6168925762176514},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6010205745697021},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5865658521652222},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5516888499259949},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5468728542327881},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5189316272735596},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4474119544029236},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4336971044540405},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4275514483451843},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3232229948043823},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2520638704299927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7910346984863281},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6808748841285706},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6782422661781311},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6280267238616943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6168925762176514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6010205745697021},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5865658521652222},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5516888499259949},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5468728542327881},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5189316272735596},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4474119544029236},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4336971044540405},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4275514483451843},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3232229948043823},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2520638704299927},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3240508.3240636","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1909316225","https://openalex.org/W2005876512","https://openalex.org/W2017416107","https://openalex.org/W2050787585","https://openalex.org/W2084053957","https://openalex.org/W2113569611","https://openalex.org/W2113968972","https://openalex.org/W2114198449","https://openalex.org/W2114770744","https://openalex.org/W2121396509","https://openalex.org/W2121927366","https://openalex.org/W2122596619","https://openalex.org/W2133665775","https://openalex.org/W2154621477","https://openalex.org/W2163146621","https://openalex.org/W2194775991","https://openalex.org/W2209874411","https://openalex.org/W2286929393","https://openalex.org/W2518599539","https://openalex.org/W2559264300","https://openalex.org/W2605929543","https://openalex.org/W2613208577","https://openalex.org/W2740982616","https://openalex.org/W2799108379","https://openalex.org/W2963091558","https://openalex.org/W2963393566","https://openalex.org/W2963470893","https://openalex.org/W2963704386","https://openalex.org/W2963951674","https://openalex.org/W2964101377","https://openalex.org/W3185334290","https://openalex.org/W4234552385","https://openalex.org/W4365799978"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W4401096132","https://openalex.org/W2990636717"],"abstract_inverted_index":{"Single":[0],"image":[1,39,153,212,220],"rain":[2,69,146],"streaks":[3,70,147],"removal":[4,71],"has":[5],"recently":[6],"witnessed":[7],"substantial":[8],"progress":[9],"due":[10],"to":[11,50,64,136,172],"the":[12,28,33,37,52,58,66,77,90,152,181,188,203,219,227],"development":[13],"of":[14,32,60,68,94,129,161,164,213],"deep":[15,21],"convolutional":[16,78,182],"neural":[17,79,95,107],"networks.":[18],"However,":[19],"existing":[20],"learning":[22,49,62],"based":[23],"methods":[24,75],"either":[25],"focus":[26,56],"on":[27,57,196,210],"entrance":[29],"and":[30,42,46,92,114,191,198],"exit":[31],"network":[34,80,96,108,125,135],"by":[35],"decomposing":[36],"input":[38],"into":[40,72,89,103],"high":[41],"low":[43],"frequency":[44],"information":[45],"employing":[47],"residual":[48],"reduce":[51],"mapping":[53,85],"range,":[54],"or":[55],"introduction":[59],"cascaded":[61],"scheme":[63],"decompose":[65],"task":[67],"multi-stages.":[73],"These":[74],"treat":[76],"as":[81],"an":[82,104],"encapsulated":[83],"end-to-end":[84,106],"module":[86],"without":[87],"deepening":[88],"rationality":[91],"superiority":[93],"design.":[97],"In":[98],"this":[99],"paper,":[100],"we":[101,119],"delve":[102],"effective":[105],"structure":[109],"for":[110,143],"stronger":[111],"feature":[112,141],"expression":[113],"spatial":[115],"correlation":[116],"learning.":[117],"Specifically,":[118],"propose":[120],"a":[121,130,162],"non-locally":[122,165],"enhanced":[123,166],"encoder-decoder":[124,134,157],"framework,":[126],"which":[127,222],"consists":[128],"pooling":[131],"indices":[132],"embedded":[133],"efficiently":[137],"learn":[138],"increasingly":[139],"abstract":[140],"representation":[142],"more":[144],"accurate":[145],"modeling":[148],"while":[149,216],"perfectly":[150],"preserving":[151,218],"detail.":[154],"The":[155],"proposed":[156,204],"framework":[158],"is":[159],"composed":[160],"series":[163],"dense":[167],"blocks":[168],"that":[169,202],"are":[170],"designed":[171],"not":[173],"only":[174],"fully":[175],"exploit":[176],"hierarchical":[177],"features":[178],"from":[179],"all":[180],"layers":[183],"but":[184],"also":[185],"well":[186,217],"capture":[187],"long-distance":[189],"dependencies":[190],"structural":[192],"information.":[193],"Extensive":[194],"experiments":[195],"synthetic":[197],"real":[199],"datasets":[200],"demonstrate":[201],"method":[205],"can":[206],"effectively":[207],"remove":[208],"rain-streaks":[209],"rainy":[211],"various":[214],"densities":[215],"details,":[221],"achieves":[223],"significant":[224],"improvements":[225],"over":[226],"recent":[228],"state-of-the-art":[229],"methods.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":43},{"year":2021,"cited_by_count":54},{"year":2020,"cited_by_count":51},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
