{"id":"https://openalex.org/W2011697680","doi":"https://doi.org/10.1109/tsp.2015.2408569","title":"Near Optimal Compressed Sensing Without Priors: Parametric SURE Approximate Message Passing","display_name":"Near Optimal Compressed Sensing Without Priors: Parametric SURE Approximate Message Passing","publication_year":2015,"publication_date":"2015-03-04","ids":{"openalex":"https://openalex.org/W2011697680","doi":"https://doi.org/10.1109/tsp.2015.2408569","mag":"2011697680"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2015.2408569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2408569","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1409.0440","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047724448","display_name":"Chunli Guo","orcid":"https://orcid.org/0000-0001-5056-7951"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Chunli Guo","raw_affiliation_strings":["Institute for Digital Communication, Edinburgh University, Edinburgh, UK","Institute for Digital Communication and with the Joint Research Institute for Signal and Image Processing, Edinburgh University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"Institute for Digital Communication, Edinburgh University, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"Institute for Digital Communication and with the Joint Research Institute for Signal and Image Processing, Edinburgh University, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057962470","display_name":"Mike E. Davies","orcid":"https://orcid.org/0000-0003-2327-236X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mike E. Davies","raw_affiliation_strings":["Institute for Digital Communication, Edinburgh University, Edinburgh, UK","Institute for Digital Communication and with the Joint Research Institute for Signal and Image Processing, Edinburgh University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"Institute for Digital Communication, Edinburgh University, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"Institute for Digital Communication and with the Joint Research Institute for Signal and Image Processing, Edinburgh University, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047724448"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":11.3914,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.99069617,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"63","issue":"8","first_page":"2130","last_page":"2141"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/message-passing","display_name":"Message passing","score":0.6482954621315002},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6106690764427185},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5785977244377136},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5514543652534485},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4840218722820282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4620141088962555},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44770342111587524},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44575971364974976},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4232845902442932},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.41573044657707214},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.41219091415405273},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.38323774933815},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.29563626646995544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14541658759117126},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14060327410697937},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0946478545665741}],"concepts":[{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.6482954621315002},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6106690764427185},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5785977244377136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5514543652534485},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4840218722820282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4620141088962555},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44770342111587524},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44575971364974976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4232845902442932},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.41573044657707214},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.41219091415405273},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38323774933815},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29563626646995544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14541658759117126},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14060327410697937},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0946478545665741},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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":4,"locations":[{"id":"doi:10.1109/tsp.2015.2408569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2408569","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1409.0440","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1409.0440","pdf_url":"https://arxiv.org/pdf/1409.0440","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.739.9457","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.739.9457","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1409.0440.pdf","raw_type":"text"},{"id":"pmh:oai:pure.ed.ac.uk:publications/69b4ad96-eb25-4f68-ab45-063a5315107a","is_oa":false,"landing_page_url":"http://hdl.handle.net/20.500.11820/69b4ad96-eb25-4f68-ab45-063a5315107a","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1409.0440","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1409.0440","pdf_url":"https://arxiv.org/pdf/1409.0440","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1493851059","https://openalex.org/W1500149156","https://openalex.org/W1540273991","https://openalex.org/W1798821657","https://openalex.org/W1822073640","https://openalex.org/W1965875863","https://openalex.org/W1987772002","https://openalex.org/W1989574703","https://openalex.org/W2007203285","https://openalex.org/W2017950357","https://openalex.org/W2018322127","https://openalex.org/W2026933032","https://openalex.org/W2043022774","https://openalex.org/W2046658845","https://openalex.org/W2054640142","https://openalex.org/W2073868986","https://openalex.org/W2079724595","https://openalex.org/W2082029531","https://openalex.org/W2083042020","https://openalex.org/W2089183963","https://openalex.org/W2100053953","https://openalex.org/W2115275122","https://openalex.org/W2116437043","https://openalex.org/W2140742952","https://openalex.org/W2141006018","https://openalex.org/W2154688547","https://openalex.org/W2212271491","https://openalex.org/W2543631487","https://openalex.org/W2610971674","https://openalex.org/W2963206527","https://openalex.org/W2964082107","https://openalex.org/W2965130990","https://openalex.org/W3098848552","https://openalex.org/W3100456593","https://openalex.org/W3100706365","https://openalex.org/W3105033759","https://openalex.org/W3124617746","https://openalex.org/W4255521522","https://openalex.org/W6632410241"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W2100805585","https://openalex.org/W2162874930"],"abstract_inverted_index":{"Both":[0],"theoretical":[1],"analysis":[2],"and":[3,72,115,162,178],"empirical":[4],"evidence":[5],"confirm":[6,208],"that":[7,182],"the":[8,37,46,59,69,74,84,90,100,106,112,119,123,126,138,145,149,154,158,169,175,183,190,202,209,212],"approximate":[9],"message":[10],"passing":[11],"(AMP)":[12],"algorithm":[13],"can":[14],"be":[15],"interpreted":[16],"as":[17,168],"recursively":[18],"solving":[19],"a":[20,30,41,51],"signal":[21,38,146],"denoising":[22],"problem:":[23],"at":[24],"each":[25,80],"AMP":[26,70,141],"iteration,":[27,83],"one":[28],"observes":[29],"Gaussian":[31],"noise":[32,43,47],"perturbed":[33],"original":[34],"signal.":[35],"Retrieving":[36],"amounts":[39],"to":[40,50,136,153],"successive":[42],"cancellation":[44],"until":[45],"variance":[48],"decreases":[49],"satisfactory":[52],"level.":[53],"In":[54,103,148],"this":[55,104],"paper,":[56,150],"we":[57,133,151],"incorporate":[58],"Stein's":[60],"unbiased":[61],"risk":[62],"estimate":[63],"(SURE)":[64],"based":[65,160],"parametric":[66,76,81,91,107,120,184,213],"denoiser":[67,85,161],"with":[68,111,174],"framework":[71],"propose":[73,163],"novel":[75],"SURE-AMP":[77,82,108,185,214],"algorithm.":[78,204],"At":[79],"is":[86,109],"adaptively":[87],"optimized":[88],"within":[89],"class":[92],"by":[93],"minimizing":[94],"SURE,":[95],"which":[96],"depends":[97],"purely":[98],"on":[99],"noisy":[101],"observation.":[102],"manner,":[105],"guaranteed":[110],"best-in-class":[113],"recovery":[114,192],"convergence":[116],"rate.":[117],"If":[118],"family":[121],"includes":[122],"families":[124,167],"of":[125,157,211],"mimimum":[127],"mean":[128],"squared":[129],"error":[130],"(MMSE)":[131],"estimators,":[132],"are":[134],"able":[135],"achieve":[137,189],"Bayesian":[139],"optimal":[140],"performance":[142],"without":[143,217],"knowing":[144],"prior.":[147],"resort":[152],"linear":[155],"parameterization":[156],"SURE":[159],"three":[164],"different":[165],"kernel":[166],"base":[170],"functions.":[171],"Numerical":[172],"simulations":[173,207],"Bernoulli-Gaussian,":[176],"k-dense":[177],"Student's-t":[179],"signals":[180,216],"demonstrate":[181],"does":[186],"not":[187],"only":[188],"state-of-the-art":[191],"but":[193],"also":[194],"runs":[195],"more":[196],"than":[197,201],"20":[198],"times":[199],"faster":[200],"EM-GM-GAMP":[203],"Natural":[205],"image":[206],"advantages":[210],"for":[215],"prior":[218],"information.":[219]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
