{"id":"https://openalex.org/W2793847116","doi":"https://doi.org/10.1109/icip.2017.8296620","title":"Synthesis-analysis deconvolutional network for compressed sensing","display_name":"Synthesis-analysis deconvolutional network for compressed sensing","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2793847116","doi":"https://doi.org/10.1109/icip.2017.8296620","mag":"2793847116"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057647276","display_name":"Qiegen Liu","orcid":"https://orcid.org/0000-0003-4717-2283"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]},{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Qiegen Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","School of Electronic Information Engineering, Nanchang University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"School of Electronic Information Engineering, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061884304","display_name":"Henry Leung","orcid":"https://orcid.org/0000-0002-5984-107X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Henry Leung","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7518,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.89210234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"23","issue":null,"first_page":"1940","last_page":"1944"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9988999962806702,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9987999796867371,"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":0.7462267875671387},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6628985404968262},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.5678622126579285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5532985925674438},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5376853346824646},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4885009527206421},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4748159646987915},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46217578649520874},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46013063192367554},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4377239942550659},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.43426308035850525},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.418854296207428},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4015335738658905},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3735477328300476},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36276066303253174},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2453291416168213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17988088726997375},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14515084028244019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462267875671387},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6628985404968262},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.5678622126579285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5532985925674438},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5376853346824646},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4885009527206421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4748159646987915},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46217578649520874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46013063192367554},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4377239942550659},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.43426308035850525},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.418854296207428},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4015335738658905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3735477328300476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36276066303253174},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2453291416168213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17988088726997375},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14515084028244019},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1635340334","https://openalex.org/W1946953458","https://openalex.org/W1971646136","https://openalex.org/W1974636210","https://openalex.org/W1991643529","https://openalex.org/W1994281301","https://openalex.org/W2018990310","https://openalex.org/W2020741214","https://openalex.org/W2033734675","https://openalex.org/W2049893860","https://openalex.org/W2056469265","https://openalex.org/W2075230492","https://openalex.org/W2079182758","https://openalex.org/W2079929986","https://openalex.org/W2107844156","https://openalex.org/W2113606819","https://openalex.org/W2114122776","https://openalex.org/W2130184048","https://openalex.org/W2131686571","https://openalex.org/W2134905716","https://openalex.org/W2141200610","https://openalex.org/W2146738562","https://openalex.org/W2149974988","https://openalex.org/W2150912629","https://openalex.org/W2160547390","https://openalex.org/W2163973643","https://openalex.org/W2167713418","https://openalex.org/W2168668658","https://openalex.org/W2169488311","https://openalex.org/W2190662802","https://openalex.org/W2202656999","https://openalex.org/W2257784670","https://openalex.org/W2293078015","https://openalex.org/W2304846792","https://openalex.org/W6676903177","https://openalex.org/W6680935700","https://openalex.org/W6682009426","https://openalex.org/W6685048874","https://openalex.org/W6697139924"],"related_works":["https://openalex.org/W4396941953","https://openalex.org/W2093104230","https://openalex.org/W2987280934","https://openalex.org/W4390874210","https://openalex.org/W4384918963","https://openalex.org/W4365211920","https://openalex.org/W2128027845","https://openalex.org/W2144332008","https://openalex.org/W3014948380","https://openalex.org/W4241564561"],"abstract_inverted_index":{"Synthesis":[0],"learning":[1],"and":[2,9,79,105,120,132,146,165],"analysis":[3,80],"learning,":[4],"with":[5,129],"sparse":[6],"coding":[7,92],"(SC)":[8],"Markov":[10],"random":[11],"fields":[12],"(MRFs)":[13],"as":[14],"two":[15,21],"representative":[16],"types":[17],"of":[18,63,72,117,143],"models,":[19],"are":[20],"complementary":[22],"tools":[23],"to":[24,52,94,108,125],"describe":[25],"the":[26,34,44,54,61,73,90,100,103,115,141],"image":[27],"manifolds.":[28],"SC":[29,119],"has":[30],"strengths":[31,116],"in":[32],"representing":[33],"regular":[35],"features/explicit":[36],"visual":[37,57],"manifolds":[38,58],"while":[39],"its":[40,160],"effectiveness":[41],"depends":[42],"on":[43,154],"training":[45,65],"dataset.":[46],"While":[47],"MRFs":[48],"have":[49],"great":[50,161],"potentials":[51,162],"characterize":[53],"stochastic":[55],"textures/implicit":[56],"but":[59,97],"at":[60],"cost":[62],"high":[64],"complexity.":[66],"In":[67],"this":[68],"paper,":[69],"by":[70,140],"means":[71],"convolutional":[74],"operator,":[75],"a":[76],"unified":[77],"synthesis":[78],"deconvolutional":[81],"network":[82],"(SADN)":[83],"is":[84,138],"presented.":[85],"It":[86],"not":[87],"only":[88],"requires":[89],"generative":[91,131],"coefficients":[93],"be":[95,109],"sparse,":[96],"also":[98],"enforces":[99],"convolution":[101],"between":[102],"filter":[104],"trained":[106],"images":[107,128],"sparse.":[110],"The":[111,135],"proposed":[112],"model":[113],"incorporates":[114],"both":[118,130,163],"MRFs,":[121],"which":[122],"enables":[123],"it":[124],"represent":[126],"general":[127],"discriminative":[133],"abilities.":[134],"resulting":[136],"minimization":[137],"tackled":[139],"combination":[142],"alternating":[144],"optimization":[145],"Iterative":[147],"Re-weighted":[148],"Least":[149],"Square":[150],"(IRLS).":[151],"Experiments":[152],"conducted":[153],"compressed":[155],"sensing":[156],"(CS)":[157],"application":[158],"show":[159],"quantitatively":[164],"qualitatively.":[166]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}