{"id":"https://openalex.org/W1980343200","doi":"https://doi.org/10.1109/icdsp.2014.6900678","title":"A split-and-merge dictionary learning algorithm for sparse representation: Application to image denoising","display_name":"A split-and-merge dictionary learning algorithm for sparse representation: Application to image denoising","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W1980343200","doi":"https://doi.org/10.1109/icdsp.2014.6900678","mag":"1980343200"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2014.6900678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2014.6900678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 19th International Conference on Digital Signal 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/A5102928532","display_name":"Subhadip Mukherjee","orcid":"https://orcid.org/0000-0002-7957-8758"},"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":true,"raw_author_name":"Subhadip Mukherjee","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Science, Bangalore, India","Department of Electrical Engineering/Indian Institute of Science/Bangalore 560012 INDIA"],"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 560012 INDIA","institution_ids":[]}]},{"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":["Department of Electrical Engineering, Indian Institute of Science, Bangalore, India","Department of Electrical Engineering/Indian Institute of Science/Bangalore 560012 INDIA"],"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 560012 INDIA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102928532"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05962542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"54","issue":null,"first_page":"310","last_page":"315"},"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":0.9998999834060669,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9976000189781189,"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/computer-science","display_name":"Computer science","score":0.7961476445198059},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.6959960460662842},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.6947745084762573},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.6743718385696411},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.5980802178382874},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5929902791976929},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5323709845542908},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5134769082069397},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4926148056983948},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.458050400018692},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44542211294174194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.413520872592926},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.08073797821998596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961476445198059},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.6959960460662842},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.6947745084762573},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.6743718385696411},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.5980802178382874},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5929902791976929},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5323709845542908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5134769082069397},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4926148056983948},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.458050400018692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44542211294174194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.413520872592926},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.08073797821998596}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdsp.2014.6900678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2014.6900678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 19th International Conference on Digital Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.iisc.ac.in:52526","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":[{"id":"https://metadata.un.org/sdg/4","score":0.8100000023841858,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W5349274","https://openalex.org/W1576854369","https://openalex.org/W1791748083","https://openalex.org/W1926138292","https://openalex.org/W1982199708","https://openalex.org/W2005876975","https://openalex.org/W2009887837","https://openalex.org/W2045328647","https://openalex.org/W2098753191","https://openalex.org/W2114083954","https://openalex.org/W2121058967","https://openalex.org/W2127271355","https://openalex.org/W2152061189","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2166790554","https://openalex.org/W2461702534","https://openalex.org/W4235713725"],"related_works":["https://openalex.org/W2509955295","https://openalex.org/W1778286912","https://openalex.org/W1987225540","https://openalex.org/W2152958724","https://openalex.org/W2099321050","https://openalex.org/W2116933539","https://openalex.org/W4245251483","https://openalex.org/W2008821896","https://openalex.org/W2034957211","https://openalex.org/W1992008660"],"abstract_inverted_index":{"In":[0],"big":[1],"data":[2,57,154],"image/video":[3],"analytics,":[4],"we":[5,44,161],"encounter":[6],"the":[7,36,50,55,69,74,88,108,111,121,127,145,152,163,177,183,199,213],"problem":[8,37,106,164],"of":[9,29,38,54,61,110,135,165,173,191,207],"learning":[10,40,105,147,179],"an":[11,46,159],"over-complete":[12],"dictionary":[13,39,104],"for":[14,84],"sparse":[15],"representation":[16],"from":[17],"a":[18,62,95,156,170,187,204],"large":[19],"training":[20,56,75,192,208],"dataset,":[21],"which":[22,149],"cannot":[23],"be":[24],"processed":[25],"at":[26,155,186],"once":[27],"because":[28],"storage":[30],"and":[31,58,80,137,140,193],"computational":[32,138],"constraints.":[33],"To":[34],"tackle":[35],"in":[41,132,189,203],"such":[42],"scenarios,":[43],"propose":[45],"algorithm":[47,70,116,129,175,201],"that":[48,126,181,198],"exploits":[49],"inherent":[51],"clustered":[52],"structure":[53],"make":[59],"use":[60,182],"divide-and-conquer":[63],"approach.":[64],"The":[65],"fundamental":[66],"idea":[67],"behind":[68],"is":[71,99,117,130],"to":[72,93,119],"partition":[73],"dataset":[76],"into":[77],"smaller":[78],"clusters,":[79],"learn":[81],"local":[82,89],"dictionaries":[83,90],"each":[85],"cluster.":[86],"Subsequently,":[87],"are":[91],"merged":[92],"form":[94],"global":[96],"dictionary.":[97],"Merging":[98],"done":[100],"by":[101],"solving":[102],"another":[103],"on":[107,142,151],"atoms":[109],"locally":[112],"trained":[113],"dictionaries.":[114],"This":[115],"referred":[118],"as":[120],"split-and-merge":[122,200],"algorithm.":[123],"We":[124,168,196],"show":[125],"proposed":[128],"efficient":[131],"its":[133],"usage":[134],"memory":[136],"complexity,":[139],"performs":[141],"par":[143],"with":[144,176],"standard":[146,178],"strategy,":[148],"operates":[150],"entire":[153,184],"time.":[157],"As":[158],"application,":[160],"consider":[162],"image":[166],"denoising.":[167],"present":[169],"comparative":[171],"analysis":[172],"our":[174],"techniques":[180],"database":[185],"time,":[188,209],"terms":[190],"denoising":[194,214],"performance.":[195,215],"observe":[197],"results":[202],"remarkable":[205],"reduction":[206],"without":[210],"significantly":[211],"affecting":[212]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
