{"id":"https://openalex.org/W4312459935","doi":"https://doi.org/10.1109/lgrs.2022.3212078","title":"Self-Supervised SAR Despeckling Powered by Implicit Deep Denoiser Prior","display_name":"Self-Supervised SAR Despeckling Powered by Implicit Deep Denoiser Prior","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312459935","doi":"https://doi.org/10.1109/lgrs.2022.3212078"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3212078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3212078","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5058405643","display_name":"Huangxing Lin","orcid":"https://orcid.org/0000-0002-2334-4987"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huangxing Lin","raw_affiliation_strings":["School of Informatics, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052754372","display_name":"Yihong Zhuang","orcid":"https://orcid.org/0000-0002-3176-3217"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihong Zhuang","raw_affiliation_strings":["School of Informatics, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100772804","display_name":"Yue Huang","orcid":"https://orcid.org/0000-0002-3913-9400"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Huang","raw_affiliation_strings":["School of Informatics, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052820597","display_name":"Xinghao Ding","orcid":"https://orcid.org/0000-0003-2288-5287"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghao Ding","raw_affiliation_strings":["School of Informatics, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5058405643"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":1.8354,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86904937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9889000058174133,"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":0.9886999726295471,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8059075474739075},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7926254272460938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7735385894775391},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.6804101467132568},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.624057948589325},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.6023245453834534},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5829907655715942},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5720260143280029},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5517305731773376},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4938369393348694},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.45847609639167786},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.44538629055023193},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43790167570114136},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.221371591091156},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.18258988857269287}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8059075474739075},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7926254272460938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7735385894775391},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.6804101467132568},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.624057948589325},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.6023245453834534},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5829907655715942},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5720260143280029},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5517305731773376},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4938369393348694},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45847609639167786},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.44538629055023193},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43790167570114136},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.221371591091156},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.18258988857269287},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3212078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3212078","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5115423372","display_name":null,"funder_award_id":"61971369","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5339618318","display_name":null,"funder_award_id":"82172033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6130862679","display_name":null,"funder_award_id":"U19B2031","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7418738665","display_name":null,"funder_award_id":"52105126","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2004376198","https://openalex.org/W2055388682","https://openalex.org/W2121927366","https://openalex.org/W2144851790","https://openalex.org/W2588852908","https://openalex.org/W2892986862","https://openalex.org/W2963583038","https://openalex.org/W2964013315","https://openalex.org/W3013302728","https://openalex.org/W3025007558","https://openalex.org/W3026889349","https://openalex.org/W3037578234","https://openalex.org/W3108438502","https://openalex.org/W3109844178","https://openalex.org/W3127482406","https://openalex.org/W3136138438","https://openalex.org/W3157976884","https://openalex.org/W3158709967","https://openalex.org/W3171007011","https://openalex.org/W3178192988","https://openalex.org/W3186988237","https://openalex.org/W3194934674","https://openalex.org/W3198480665","https://openalex.org/W4213150710","https://openalex.org/W4226012892","https://openalex.org/W4312624488","https://openalex.org/W6749271710"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2055824452","https://openalex.org/W2121688719","https://openalex.org/W2727313114","https://openalex.org/W2016481886","https://openalex.org/W4241911733"],"abstract_inverted_index":{"Speckle":[0],"removal":[1],"is":[2,83,100,116],"an":[3],"important":[4],"preprocessing":[5],"step":[6],"for":[7,38],"synthetic":[8,151],"aperture":[9],"radar":[10],"(SAR)":[11],"imaging.":[12],"Since":[13],"speckle-free":[14],"SAR":[15,39,154],"images":[16],"do":[17],"not":[18,23],"exist,":[19],"supervised":[20],"methods":[21,148],"are":[22],"applicable.":[24],"In":[25],"this":[26],"letter,":[27],"we":[28,65,76],"propose":[29],"implicit":[30],"deep":[31,44],"denoiser":[32],"prior":[33,46],"(SAR-IDDP),":[34],"a":[35,43,73],"self-supervised":[36,146],"method":[37,124],"despeckling.":[40],"SAR-IDDP":[41,138],"uses":[42],"image":[45,93,133],"(DIP)":[47],"implicitly":[48],"captured":[49],"by":[50],"the":[51,67,70,87,90,104,111],"convolutional":[52],"neural":[53],"network":[54],"(CNN)":[55],"to":[56,85,130],"formulate":[57],"regularization":[58],"instead":[59],"of":[60,69,106,114,122],"traditional":[61],"hand-crafted":[62],"priors.":[63],"Specifically,":[64],"treat":[66],"output":[68],"CNN":[71,82],"as":[72],"\u201cprior\u201d":[74],"that":[75,137],"denoise":[77],"again":[78,91],"after":[79],"\u201crenoising.\u201d":[80],"The":[81,97,119],"updated":[84],"maximize":[86],"similarity":[88],"between":[89],"denoised":[92],"and":[94,145,152],"its":[95],"prior.":[96],"renoising":[98],"procedure":[99],"designed":[101],"based":[102],"on":[103,149],"assumption":[105],"unit":[107],"mean":[108],"noise,":[109],"while":[110],"spatial":[112],"correlation":[113],"speckle":[115],"also":[117],"involved.":[118],"despeckling":[120,147],"ability":[121],"our":[123],"stems":[125],"from":[126],"CNN\u2019s":[127],"natural":[128],"tendency":[129],"capture":[131],"low-level":[132],"statistics.":[134],"Experiments":[135],"show":[136],"achieves":[139],"significant":[140],"improvements":[141],"over":[142],"existing":[143],"model-based":[144],"both":[150],"real":[153],"images.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
