{"id":"https://openalex.org/W4214666416","doi":"https://doi.org/10.1109/lgrs.2022.3155651","title":"Learned Compression Framework With Pyramidal Features and Quality Enhancement for SAR Images","display_name":"Learned Compression Framework With Pyramidal Features and Quality Enhancement for SAR Images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4214666416","doi":"https://doi.org/10.1109/lgrs.2022.3155651"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3155651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3155651","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/A5000972803","display_name":"Zhixiong Di","orcid":"https://orcid.org/0000-0001-7323-5052"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixiong Di","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-7323-5052","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018466767","display_name":"Xuan Chen","orcid":"https://orcid.org/0000-0002-4153-7782"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Chen","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-4153-7782","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003423239","display_name":"Qiang Wu","orcid":"https://orcid.org/0000-0001-9339-0762"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Wu","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000583035","display_name":"Jiangyi Shi","orcid":"https://orcid.org/0000-0002-8465-9528"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyi Shi","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi\u2019an, China","School of Microelectronics, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-8465-9528","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101608790","display_name":"Quanyuan Feng","orcid":"https://orcid.org/0000-0003-2487-8072"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanyuan Feng","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085004179","display_name":"Yibo Fan","orcid":"https://orcid.org/0000-0003-2523-8261"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibo Fan","raw_affiliation_strings":["State Key Laboratory of ASIC and System, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2523-8261","affiliations":[{"raw_affiliation_string":"State Key Laboratory of ASIC and System, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9747,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.73617366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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":0.9994000196456909,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.998199999332428,"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":0.7723376750946045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6820557117462158},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6772431135177612},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5510386824607849},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.5175877809524536},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5018553733825684},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.49657541513442993},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4834050238132477},{"id":"https://openalex.org/keywords/codec","display_name":"Codec","score":0.4651046693325043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4504311680793762},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44938981533050537},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4436595141887665},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4309346079826355},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.4257766008377075},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.32766473293304443},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.25463348627090454},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.1862570345401764},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13345256447792053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7723376750946045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6820557117462158},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6772431135177612},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5510386824607849},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.5175877809524536},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5018553733825684},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.49657541513442993},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4834050238132477},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.4651046693325043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4504311680793762},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44938981533050537},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4436595141887665},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4309346079826355},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.4257766008377075},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32766473293304443},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25463348627090454},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.1862570345401764},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13345256447792053},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3155651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3155651","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":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1421920744","display_name":null,"funder_award_id":"2020YFG0452","funder_id":"https://openalex.org/F4320333335","funder_display_name":"Sichuan Province Science and Technology Support Program"},{"id":"https://openalex.org/G6184139127","display_name":null,"funder_award_id":"62090012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8477318590","display_name":null,"funder_award_id":"61504110","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G995454570","display_name":null,"funder_award_id":"2019YFG0092","funder_id":"https://openalex.org/F4320333335","funder_display_name":"Sichuan Province Science and Technology Support Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320333335","display_name":"Sichuan Province Science and Technology Support Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2030265202","https://openalex.org/W2140196014","https://openalex.org/W2183341477","https://openalex.org/W2520820497","https://openalex.org/W2552465432","https://openalex.org/W2564046962","https://openalex.org/W2621042270","https://openalex.org/W2734753265","https://openalex.org/W2757678917","https://openalex.org/W2785562966","https://openalex.org/W2931697294","https://openalex.org/W2949361041","https://openalex.org/W2962676454","https://openalex.org/W2964098744","https://openalex.org/W2964101377","https://openalex.org/W2982836648","https://openalex.org/W2982853315","https://openalex.org/W3004685385","https://openalex.org/W3034469748","https://openalex.org/W3114364469","https://openalex.org/W3131427579","https://openalex.org/W3135879802","https://openalex.org/W3185299144","https://openalex.org/W6738494155","https://openalex.org/W6754634825","https://openalex.org/W6767475311","https://openalex.org/W6767508033","https://openalex.org/W6773431304"],"related_works":["https://openalex.org/W2151920332","https://openalex.org/W2382458395","https://openalex.org/W2350939138","https://openalex.org/W2390873329","https://openalex.org/W2151403598","https://openalex.org/W2385039112","https://openalex.org/W2137460601","https://openalex.org/W2354616978","https://openalex.org/W2147992969","https://openalex.org/W1575921859"],"abstract_inverted_index":{"Current":[0],"image":[1,43],"compression":[2,29,49],"algorithms":[3],"based":[4],"on":[5,55,153],"transforms":[6],"can":[7],"achieve":[8],"ideal":[9],"performance":[10],"for":[11,91],"natural":[12],"images,":[13],"but":[14],"do":[15,17],"not":[16],"well":[18],"with":[19,31,87],"synthetic":[20],"aperture":[21],"radar":[22],"(SAR)":[23],"images.":[24],"We":[25],"propose":[26],"a":[27,88,117,139],"learned":[28],"framework":[30,137],"pyramidal":[32,61],"features":[33],"and":[34,45,51,73,94,128,150,157],"quality":[35],"enhancement":[36],"to":[37,46,69,102,124],"fully":[38,125],"exploit":[39],"the":[40,48,56,66,83,104,109,135,155],"redundancy":[41],"among":[42],"pixels":[44],"improve":[47,114],"bitrate":[50],"reconstruction":[52,115],"quality.":[53],"Based":[54],"variational":[57],"autoencoder":[58,68,86],"(VAE)":[59],"architecture,":[60],"decomposition":[62],"is":[63,80,100,122],"performed":[64],"at":[65],"first":[67],"extract":[70],"both":[71,154],"global":[72,129],"coarse":[74],"feature":[75],"maps.":[76],"The":[77],"latent":[78],"distribution":[79],"modeled":[81],"by":[82],"second":[84],"hyperprior":[85,110],"single-Gaussian":[89],"model":[90],"more":[92],"accurate":[93],"flexible":[95],"entropy":[96,105],"estimation.":[97],"Universal":[98],"quantization":[99],"applied":[101],"consolidate":[103],"estimation":[106],"accuracy":[107],"of":[108],"network.":[111],"To":[112],"further":[113],"quality,":[116],"residual":[118],"dense":[119],"network":[120],"(RDN)":[121],"adopted":[123],"capture":[126],"local":[127],"features.":[130],"Experimental":[131],"results":[132],"demonstrate":[133],"that":[134],"proposed":[136],"provides":[138],"better":[140],"rate-distortion":[141],"tradeoff":[142],"than":[143],"standard":[144],"codecs":[145],"such":[146],"as":[147],"JPEG,":[148],"JPEG2000,":[149],"learning-based":[151],"methods":[152],"Sandia":[156],"ICEYE":[158],"datasets.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}