{"id":"https://openalex.org/W4362564513","doi":"https://doi.org/10.1145/3577164.3577175","title":"wganBCS: Block-wise Image Compressive Sensing","display_name":"wganBCS: Block-wise Image Compressive Sensing","publication_year":2022,"publication_date":"2022-11-25","ids":{"openalex":"https://openalex.org/W4362564513","doi":"https://doi.org/10.1145/3577164.3577175"},"language":"en","primary_location":{"id":"doi:10.1145/3577164.3577175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577164.3577175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577164.3577175","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3577164.3577175","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Boyan Chen","orcid":"https://orcid.org/0000-0002-0927-0328"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Boyan Chen","raw_affiliation_strings":["cis, Hosei University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-0927-0328","affiliations":[{"raw_affiliation_string":"cis, Hosei University, Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052554146","display_name":"Kaoru Uchida","orcid":"https://orcid.org/0000-0002-2037-0936"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaoru Uchida","raw_affiliation_strings":["cis, Hosei University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2037-0936","affiliations":[{"raw_affiliation_string":"cis, Hosei University, Japan","institution_ids":["https://openalex.org/I204291657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204291657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38596249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"68","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":0.9995999932289124,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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/compressed-sensing","display_name":"Compressed sensing","score":0.7438870668411255},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.7278330326080322},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7192534804344177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6771978139877319},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5354087352752686},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4693748950958252},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4503539502620697},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4436531662940979},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4344308376312256},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4301298260688782},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.42022499442100525},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4181733727455139},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4172028601169586},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21990466117858887},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.09259501099586487}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7438870668411255},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.7278330326080322},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7192534804344177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6771978139877319},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5354087352752686},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4693748950958252},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4503539502620697},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4436531662940979},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4344308376312256},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4301298260688782},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.42022499442100525},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4181733727455139},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4172028601169586},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21990466117858887},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.09259501099586487},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3577164.3577175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577164.3577175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577164.3577175","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3577164.3577175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577164.3577175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577164.3577175","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8062121128","display_name":"Extraction and Identification of Micro Features on Surface Images","funder_award_id":"20K11777","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4362564513.pdf","grobid_xml":"https://content.openalex.org/works/W4362564513.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2002969407","https://openalex.org/W2015418199","https://openalex.org/W2045737896","https://openalex.org/W2119667497","https://openalex.org/W2273561594","https://openalex.org/W2695031829","https://openalex.org/W2963312584","https://openalex.org/W2964178808","https://openalex.org/W3107867397","https://openalex.org/W3115447952","https://openalex.org/W3195968859","https://openalex.org/W4295521014","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6679164396","https://openalex.org/W6735913928","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2896778670","https://openalex.org/W2757389719","https://openalex.org/W2951714568","https://openalex.org/W1963814553","https://openalex.org/W2037595954","https://openalex.org/W1988158806","https://openalex.org/W2507293823","https://openalex.org/W2386146599","https://openalex.org/W2018015402","https://openalex.org/W2144778520"],"abstract_inverted_index":{"Block-wise":[0],"compressive":[1,26,79,117,160,261],"sensing":[2,8,27,37,51,80,118,262],"(BCS)":[3],"uses":[4],"a":[5,112,125,143,204],"fix":[6],"size":[7],"matrix":[9],"to":[10,15,62,88,137,149,181,203,207,225,232,254],"raster-scan":[11],"the":[12,17,33,57,65,72,94,132,139,152,157,165,169,176,183,195,199,209,213,217,227,249,255],"entire":[13],"image":[14,25,66,78,116,250,260],"acquire":[16],"signal":[18],"block":[19,54,76,114,153,158],"by":[20,156],"block.":[21],"Compared":[22],"with":[23,151],"traditional":[24,128],"methods,":[28],"BCS":[29,40,108],"can":[30],"greatly":[31],"reduce":[32],"memory":[34],"consumption":[35],"of":[36,74,127,258],"matrix.":[38],"However,":[39],"paradigm":[41,109],"still":[42],"suffers":[43],"from":[44,198],"two":[45],"issues.":[46],"One":[47],"is":[48,70,242],"that":[49,71,239],"block-wise":[50],"causes":[52,93],"heavy":[53],"effect":[55,154],"on":[56,174],"reconstructed":[58,95,188,228],"image,":[59],"which":[60,92,124],"leads":[61],"degradation":[63],"in":[64,245],"quality":[67,251],"metrics.":[68],"Another":[69],"state":[73,257],"art":[75,259],"wise":[77,115,159],"methods":[81],"use":[82,142],"mean":[83,177],"square":[84,178],"error":[85,179],"loss":[86,130,134,180],"function":[87],"optimize":[89,138],"their":[90,191],"models,":[91],"images":[96,189,197,229],"over":[97],"smoothed.":[98],"In":[99],"this":[100,222],"paper,":[101],"we":[102],"incorporate":[103],"generative":[104],"adversarial":[105],"training":[106],"into":[107],"and":[110,119,131,190,248],"propose":[111],"new":[113],"reconstruction":[120],"model":[121,241],"called":[122],"wganBCS,":[123],"combination":[126],"L2":[129],"Wasserstein":[133,145,210,223],"are":[135,201],"used":[136],"model.":[140],"We":[141],"modified":[144],"GAN":[146],"(WGAN)":[147],"network":[148,171,206,219],"deal":[150],"caused":[155],"sensing.":[161],"Specifically":[162],"speaking,":[163],"at":[164],"first":[166],"stage":[167],"training,":[168],"generator":[170,200,218],"will":[172,220],"focus":[173],"minimize":[175,221],"keep":[182,226],"overall":[184],"pixel":[185],"accuracy":[186],"between":[187,212],"ground":[192,214,233],"truths.":[193],"Then":[194],"output":[196],"sent":[202],"critic":[205],"calculate":[208],"distance":[211,224],"truth":[215,234],"images,":[216],"visually":[230],"authentic":[231],"images.":[235],"Experimental":[236],"result":[237],"shows":[238],"our":[240],"superior":[243],"both":[244],"visual":[246],"authenticity":[247],"metrics":[252],"compared":[253],"most":[256],"methods.":[263]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
