{"id":"https://openalex.org/W7081999532","doi":"https://doi.org/10.1109/lsp.2025.3609639","title":"Untrained Network Prior With Spectral Bias Compensation for Speckle Removal in AS-OCT","display_name":"Untrained Network Prior With Spectral Bias Compensation for Speckle Removal in AS-OCT","publication_year":2025,"publication_date":"2025-09-12","ids":{"openalex":"https://openalex.org/W7081999532","doi":"https://doi.org/10.1109/lsp.2025.3609639"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2025.3609639","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3609639","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing 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":null,"display_name":"Sanqian Li","orcid":"https://orcid.org/0009-0007-9076-247X"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sanqian Li","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang, China"],"raw_orcid":"https://orcid.org/0009-0007-9076-247X","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dehan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dehan Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Muxing Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muxing Xiong","raw_affiliation_strings":["Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Risa Higashita","orcid":"https://orcid.org/0000-0002-8160-2841"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Risa Higashita","raw_affiliation_strings":["Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-8160-2841","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jiang Liu","orcid":"https://orcid.org/0000-0001-6281-6505"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Liu","raw_affiliation_strings":["Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-6281-6505","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60068211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"973","last_page":"977"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6784999966621399,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6784999966621399,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13067","display_name":"Geological Modeling and Analysis","score":0.02930000051856041,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.01810000091791153,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.8618000149726868},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.7195000052452087},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5812000036239624},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.569599986076355},{"id":"https://openalex.org/keywords/optical-coherence-tomography","display_name":"Optical coherence tomography","score":0.5498999953269958},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5184000134468079},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.517300009727478},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4648999869823456},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4498000144958496},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4458000063896179}],"concepts":[{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.8618000149726868},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.7195000052452087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699000000953674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6456000208854675},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5812000036239624},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.569599986076355},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5278000235557556},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5184000134468079},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4648999869823456},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.35499998927116394},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.30809998512268066},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3609639","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3609639","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W217075555","https://openalex.org/W1970003143","https://openalex.org/W1998339281","https://openalex.org/W2032476163","https://openalex.org/W2047781078","https://openalex.org/W2061240737","https://openalex.org/W2090563002","https://openalex.org/W2097073572","https://openalex.org/W2111899019","https://openalex.org/W2133665775","https://openalex.org/W2151035455","https://openalex.org/W2166086209","https://openalex.org/W2224227718","https://openalex.org/W2344288453","https://openalex.org/W2769757464","https://openalex.org/W2963383962","https://openalex.org/W3021094251","https://openalex.org/W3030238474","https://openalex.org/W3085038231","https://openalex.org/W3121054072","https://openalex.org/W3151666947","https://openalex.org/W3178474055","https://openalex.org/W3203605797","https://openalex.org/W4220808400","https://openalex.org/W4372260382","https://openalex.org/W4387211422","https://openalex.org/W4403390320","https://openalex.org/W4403600676"],"related_works":[],"abstract_inverted_index":{"Speckle":[0],"removal":[1,37,123],"in":[2,26,38],"anterior":[3],"segment":[4],"optical":[5],"coherence":[6],"tomography":[7],"(AS-OCT)":[8],"images":[9,41],"is":[10],"a":[11,113,126],"nonlinear":[12,119],"inverse":[13,27],"problem":[14],"that":[15],"improves":[16],"image":[17,74],"quality.":[18],"Although":[19],"untrained":[20],"network":[21,70,139],"priors":[22],"have":[23],"proven":[24],"effective":[25],"restoration":[28],"tasks":[29],"but":[30],"are":[31,56,160],"not":[32],"directly":[33],"applicable":[34],"to":[35,43,66,116,130],"speckle":[36,122,136],"clinical":[39,59],"AS-OCT":[40,73],"due":[42],"spectral":[44],"bias:":[45],"networks":[46],"focus":[47],"more":[48],"on":[49],"low-frequency":[50],"information,":[51],"whereas":[52],"high-frequency":[53,96],"structural":[54,84,97,133,154],"details":[55,134],"vital":[57],"for":[58,72,92,138],"analysis.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,90,110],"aim":[65],"boost":[67],"the":[68,83,93,105,118,132,143,146],"Untrained":[69],"Priors":[71],"despeckling":[75,150],"by":[76],"Spectral":[77],"Bias":[78],"Compensation":[79],"(UP-SBC),":[80],"which":[81],"enriches":[82],"information":[85,98],"and":[86,99,124,135,158],"overall":[87],"regularization.":[88],"Specifically,":[89],"compensate":[91],"loss":[94,129],"of":[95,121,145],"analyze":[100],"its":[101],"efficacy":[102,144],"via":[103],"analyzing":[104],"frequency":[106,128],"band":[107],"correspondence.":[108],"Then,":[109],"further":[111],"design":[112],"data":[114,157],"fidelity":[115],"regularize":[117],"nature":[120],"incorporate":[125],"focal":[127],"decouple":[131],"noise":[137],"optimization.":[140],"Experiments":[141],"verify":[142],"UP-SBC,":[147],"providing":[148],"high-quality":[149],"results":[151],"while":[152],"preserving":[153],"details.":[155],"The":[156],"code":[159],"available":[161],"at":[162],"<uri":[163],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[164],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/lisanqian1212/UP-SBC</uri>.":[165]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
