{"id":"https://openalex.org/W1936408885","doi":"https://doi.org/10.1109/icip.2015.7350769","title":"Image denoising using optimally weighted bilateral filters: A sure and fast approach","display_name":"Image denoising using optimally weighted bilateral filters: A sure and fast approach","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W1936408885","doi":"https://doi.org/10.1109/icip.2015.7350769","mag":"1936408885"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7350769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7350769","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-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/A5045035201","display_name":"Kunal N. Chaudhury","orcid":"https://orcid.org/0000-0002-8136-605X"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunal N. Chaudhury","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Science, Bangalore, India","Department of Electrical Engineering, Indian Institute of Science,  Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]},{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Science,  Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075042099","display_name":"Kollipara Rithwik","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kollipara Rithwik","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8082,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.93671655,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9990000128746033,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9879999756813049,"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/noise-reduction","display_name":"Noise reduction","score":0.6802423000335693},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.656649649143219},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6286585927009583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.542987048625946},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4902789890766144},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4535069465637207},{"id":"https://openalex.org/keywords/filtering-theory","display_name":"Filtering theory","score":0.4158879816532135}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6802423000335693},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.656649649143219},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6286585927009583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.542987048625946},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4902789890766144},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4535069465637207},{"id":"https://openalex.org/C2988922011","wikidata":"https://www.wikidata.org/wiki/Q5449244","display_name":"Filtering theory","level":2,"score":0.4158879816532135}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2015.7350769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7350769","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W59771946","https://openalex.org/W1494195692","https://openalex.org/W1979139980","https://openalex.org/W1995194116","https://openalex.org/W1998419211","https://openalex.org/W2054640142","https://openalex.org/W2056370875","https://openalex.org/W2070009059","https://openalex.org/W2097073572","https://openalex.org/W2099244020","https://openalex.org/W2103559027","https://openalex.org/W2106884232","https://openalex.org/W2107462260","https://openalex.org/W2108382860","https://openalex.org/W2113824004","https://openalex.org/W2113945798","https://openalex.org/W2140742952","https://openalex.org/W2150134853","https://openalex.org/W2153663612","https://openalex.org/W2156242833","https://openalex.org/W2160715448","https://openalex.org/W2163262991","https://openalex.org/W7070871703"],"related_works":["https://openalex.org/W2895947835","https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W2387428419","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W2386722878","https://openalex.org/W2353444452","https://openalex.org/W2001438600","https://openalex.org/W2499707420"],"abstract_inverted_index":{"The":[0,20,85,142],"bilateral":[1,78,212],"filter":[2,42,87,144,195,213],"is":[3,27,88,145,196,214],"known":[4,28],"to":[5,29,49,90,100,121,133,148],"be":[6,91],"quite":[7],"effective":[8],"in":[9,35,46,124,157,201],"denoising":[10,21,74,179,207],"images":[11,184],"corrupted":[12],"with":[13,32,216],"small":[14],"dosages":[15],"of":[16,23,40,76,112,153,159,203,209],"additive":[17],"Gaussian":[18],"noise.":[19],"performance":[22,75,208],"the":[24,33,41,47,73,77,110,113,116,129,138,154,160,173,190,193,206,210,217],"filter,":[25,79,118],"however,":[26],"degrade":[30],"quickly":[31],"increase":[34],"noise":[36,95,106,164],"level.":[37],"Several":[38],"adaptations":[39],"have":[43],"been":[44],"proposed":[45],"literature":[48],"address":[50],"this":[51,61],"shortcoming,":[52],"but":[53],"often":[54,98],"at":[55,80,93,162],"a":[56,65,104,125,169],"substantial":[57],"computational":[58],"overhead.":[59],"In":[60],"paper,":[62],"we":[63,119],"report":[64],"simple":[66],"pre-processing":[67],"step":[68],"that":[69,189],"can":[70],"substantially":[71],"improve":[72],"almost":[81],"no":[82],"additional":[83],"cost.":[84],"modified":[86,117],"designed":[89],"robust":[92],"large":[94],"levels,":[96],"and":[97,115,177,200],"tends":[99],"perform":[101,149],"poorly":[102],"below":[103],"certain":[105],"threshold.":[107],"To":[108],"get":[109],"best":[111],"original":[114,194],"propose":[120],"combine":[122],"them":[123],"weighted":[126,174],"fashion,":[127],"where":[128],"weights":[130],"are":[131,185],"chosen":[132],"minimize":[134],"(a":[135],"surrogate":[136],"of)":[137],"oracle":[139],"mean-squared-error":[140],"(MSE).":[141],"optimally-weighted":[143,211],"thus":[146],"guaranteed":[147],"better":[150],"than":[151],"either":[152],"component":[155],"filters":[156],"terms":[158,202],"MSE,":[161],"all":[163],"levels.":[165],"We":[166],"also":[167],"provide":[168],"fast":[170],"algorithm":[171],"for":[172],"filtering.":[175],"Visual":[176],"quantitative":[178],"results":[180],"on":[181],"standard":[182],"test":[183],"reported":[186],"which":[187],"demonstrate":[188],"improvement":[191],"over":[192],"significant":[197],"both":[198],"visually":[199],"PSNR.":[204],"Moreover,":[205],"competitive":[215],"computation-intensive":[218],"non-local":[219],"means":[220],"filter.":[221]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
