{"id":"https://openalex.org/W2563666381","doi":"https://doi.org/10.1145/3009977.3010025","title":"A distribution-independent risk estimator for image denoising","display_name":"A distribution-independent risk estimator for image denoising","publication_year":2016,"publication_date":"2016-12-18","ids":{"openalex":"https://openalex.org/W2563666381","doi":"https://doi.org/10.1145/3009977.3010025","mag":"2563666381"},"language":"en","primary_location":{"id":"doi:10.1145/3009977.3010025","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3009977.3010025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing","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/A5024356325","display_name":"Babu Kishore Subramanian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Babu Kishore Subramanian","raw_affiliation_strings":["Sattva MedTech, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Sattva MedTech, Bangalore, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102003565","display_name":"Ashutosh Gupta","orcid":"https://orcid.org/0009-0004-2923-0851"},"institutions":[{"id":"https://openalex.org/I1289461252","display_name":"Indian Space Research Organisation","ror":"https://ror.org/00cwrns71","country_code":"IN","type":"government","lineage":["https://openalex.org/I1289461252","https://openalex.org/I3148377317"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashutosh Gupta","raw_affiliation_strings":["Indian Space Research Organisation, Ahmedabad, India"],"affiliations":[{"raw_affiliation_string":"Indian Space Research Organisation, Ahmedabad, India","institution_ids":["https://openalex.org/I1289461252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005652970","display_name":"Chandra Sekhar Seelamantula","orcid":"https://orcid.org/0000-0001-9049-1912"},"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":"Chandra Sekhar Seelamantula","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024356325"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14185636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9868000149726868,"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":0.9703999757766724,"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/mathematics","display_name":"Mathematics","score":0.6557671427726746},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5864751935005188},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5737117528915405},{"id":"https://openalex.org/keywords/steins-unbiased-risk-estimate","display_name":"Stein's unbiased risk estimate","score":0.5275684595108032},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5169926881790161},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.487007200717926},{"id":"https://openalex.org/keywords/laplace-distribution","display_name":"Laplace distribution","score":0.4756298363208771},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.47330084443092346},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.46576523780822754},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4515863060951233},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4354168772697449},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4353664517402649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34970709681510925},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.3484649658203125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3043709099292755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2993665635585785},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20363682508468628},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.15706172585487366}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6557671427726746},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5864751935005188},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5737117528915405},{"id":"https://openalex.org/C134962040","wikidata":"https://www.wikidata.org/wiki/Q7606742","display_name":"Stein's unbiased risk estimate","level":5,"score":0.5275684595108032},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5169926881790161},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.487007200717926},{"id":"https://openalex.org/C183057437","wikidata":"https://www.wikidata.org/wiki/Q671617","display_name":"Laplace distribution","level":3,"score":0.4756298363208771},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.47330084443092346},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.46576523780822754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4515863060951233},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4354168772697449},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4353664517402649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34970709681510925},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.3484649658203125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3043709099292755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2993665635585785},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20363682508468628},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.15706172585487366},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3009977.3010025","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3009977.3010025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.iisc.ac.in:57431","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196309","display_name":"NOT FOUND REPOSITORY (Indian Institute of Science Bangalore)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I59270414","host_organization_name":"Indian Institute of Science Bangalore","host_organization_lineage":["https://openalex.org/I59270414"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceedings"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1938002148","https://openalex.org/W1987007540","https://openalex.org/W2002022178","https://openalex.org/W2007203285","https://openalex.org/W2012489043","https://openalex.org/W2056370875","https://openalex.org/W2079724595","https://openalex.org/W2097073572","https://openalex.org/W2099244020","https://openalex.org/W2103559027","https://openalex.org/W2108855378","https://openalex.org/W2119634769","https://openalex.org/W2125527601","https://openalex.org/W2133665775","https://openalex.org/W2140742952","https://openalex.org/W2144451417","https://openalex.org/W2145984041","https://openalex.org/W2146842127","https://openalex.org/W2158940042","https://openalex.org/W2160547390","https://openalex.org/W2163262991","https://openalex.org/W2163446914","https://openalex.org/W2170885533","https://openalex.org/W2294479150","https://openalex.org/W3105835428"],"related_works":["https://openalex.org/W2035998077","https://openalex.org/W4239491110","https://openalex.org/W2349547417","https://openalex.org/W4210503132","https://openalex.org/W4237435333","https://openalex.org/W2910434125","https://openalex.org/W2059813665","https://openalex.org/W1911817635","https://openalex.org/W2802667153","https://openalex.org/W3170634232"],"abstract_inverted_index":{"We":[0,23,85,108,120],"address":[1],"the":[2,18,28,35,56,61,72,105,111,115,122,129,136,150,156,162],"problem":[3],"of":[4,21,26,60,71,104,124,149,161,191],"image":[5,43],"denoising":[6,137],"for":[7,42,55,98,170,179],"an":[8,53,68,87],"additive":[9,64,99],"white":[10,65,100],"noise":[11,32,49,101,106,172,181],"model":[12,102],"without":[13,91],"placing":[14],"any":[15],"restrictions":[16],"on":[17,82,166],"statistical":[19],"distribution":[20,50,188],"noise.":[22],"assume":[24],"knowledge":[25],"only":[27],"first-":[29],"and":[30,51,174,186,194],"second-order":[31],"statistics.":[33],"In":[34],"recent":[36],"mean-square":[37],"error":[38],"(MSE)":[39],"minimization":[40],"approaches":[41],"denoising,":[44],"one":[45],"considers":[46],"a":[47],"particular":[48],"derives":[52],"expression":[54],"unbiased":[57,69,76,88],"risk":[58,77,89,117],"estimate":[59,70,78,90,113,118],"MSE.":[62,158],"For":[63],"Gaussian":[66,171],"noise,":[67],"MSE":[73,112],"is":[74,145,165],"Stein's":[75,83,93],"(SURE),":[79],"which":[80],"relies":[81],"lemma.":[84],"derive":[86],"using":[92,126,143],"lemma":[94],"or":[95],"its":[96],"counterparts":[97],"irrespective":[103],"distribution.":[107],"refer":[109],"to":[110],"as":[114,135,184],"generic":[116],"(GenRE).":[119],"demonstrate":[121],"effectiveness":[123],"GenRE":[125,144],"shrinkage":[127],"in":[128,189],"undecimated":[130],"Haar":[131],"wavelet":[132],"transform":[133],"domain":[134],"function.":[138],"The":[139,159],"estimated":[140],"peak-signal-to-noise-ratio":[141],"(PSNR)":[142],"typically":[146],"within":[147],"1%":[148],"PSNR":[151,193],"obtained":[152],"when":[153],"optimizing":[154],"with":[155,168],"oracle":[157],"performance":[160],"proposed":[163],"method":[164],"par":[167],"SURE":[169],"distribution,":[173],"better":[175],"than":[176],"SURE-based":[177],"methods":[178],"other":[180],"distributions":[182],"such":[183],"uniform":[185],"Laplacian":[187],"terms":[190],"both":[192],"structural":[195],"similarity":[196],"(SSIM).":[197]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}