{"id":"https://openalex.org/W3193501362","doi":"https://doi.org/10.1109/iccta40200.2016.9513236","title":"Low Dose CT Image Restoration by Incremental Learning and Ant Colony Optimization","display_name":"Low Dose CT Image Restoration by Incremental Learning and Ant Colony Optimization","publication_year":2016,"publication_date":"2016-10-25","ids":{"openalex":"https://openalex.org/W3193501362","doi":"https://doi.org/10.1109/iccta40200.2016.9513236","mag":"3193501362"},"language":"en","primary_location":{"id":"doi:10.1109/iccta40200.2016.9513236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9513236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","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/A5101915562","display_name":"Mohamed Hashem","orcid":"https://orcid.org/0000-0001-8824-8817"},"institutions":[{"id":"https://openalex.org/I107720978","display_name":"Ain Shams University","ror":"https://ror.org/00cb9w016","country_code":"EG","type":"education","lineage":["https://openalex.org/I107720978"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Mohamed Hashem","raw_affiliation_strings":["Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt"],"affiliations":[{"raw_affiliation_string":"Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt","institution_ids":["https://openalex.org/I107720978"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056879425","display_name":"Essam A. Rashed","orcid":"https://orcid.org/0000-0001-6571-9807"},"institutions":[{"id":"https://openalex.org/I114794399","display_name":"Suez Canal University","ror":"https://ror.org/02m82p074","country_code":"EG","type":"education","lineage":["https://openalex.org/I114794399"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Essam A. Rashed","raw_affiliation_strings":["Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt","institution_ids":["https://openalex.org/I114794399"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013364621","display_name":"Fatma Harby Mohamed","orcid":null},"institutions":[{"id":"https://openalex.org/I114794399","display_name":"Suez Canal University","ror":"https://ror.org/02m82p074","country_code":"EG","type":"education","lineage":["https://openalex.org/I114794399"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Fatma Harby Mohamed","raw_affiliation_strings":["Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt","institution_ids":["https://openalex.org/I114794399"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101915562"],"corresponding_institution_ids":["https://openalex.org/I107720978"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.29640693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"39","issue":null,"first_page":"68","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9986000061035156,"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":0.9986000061035156,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9933000206947327,"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/computer-science","display_name":"Computer science","score":0.6499239206314087},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6457585096359253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5927777886390686},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5357021689414978},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.50641930103302},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4780586063861847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46181759238243103},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.45958203077316284},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.4560943841934204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.262391597032547},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.14048266410827637},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.12742570042610168}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6499239206314087},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6457585096359253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5927777886390686},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5357021689414978},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.50641930103302},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4780586063861847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46181759238243103},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.45958203077316284},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.4560943841934204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.262391597032547},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.14048266410827637},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.12742570042610168},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccta40200.2016.9513236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9513236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4699999988079071,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1506482396","https://openalex.org/W1570876803","https://openalex.org/W1573676079","https://openalex.org/W2003624223","https://openalex.org/W2015245929","https://openalex.org/W2049633694","https://openalex.org/W2058887147","https://openalex.org/W2094438952","https://openalex.org/W2114759290","https://openalex.org/W2116692144","https://openalex.org/W2118292158","https://openalex.org/W2132549764","https://openalex.org/W2133665775","https://openalex.org/W2136396015","https://openalex.org/W2171697262","https://openalex.org/W2172275395","https://openalex.org/W2536599074","https://openalex.org/W4299796063","https://openalex.org/W6728784816","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W1839961359","https://openalex.org/W2473373438","https://openalex.org/W73430451","https://openalex.org/W2368486525","https://openalex.org/W2075146114","https://openalex.org/W1783992599","https://openalex.org/W2114899076","https://openalex.org/W2077224612","https://openalex.org/W2100805585","https://openalex.org/W2153481672"],"abstract_inverted_index":{"Patch":[0],"priors":[1],"play":[2],"a":[3,18,36,53,61,90,113,205],"key":[4],"issue":[5],"in":[6,39,101,109,194],"patch":[7,30,201],"based":[8],"image":[9,22,26,40,116],"restoration":[10,41],"tasks.":[11],"The":[12,121,220],"main":[13],"idea":[14,123],"is":[15,124,158,177,209],"to":[16,98,111,125,132,179,185,189,211,224],"learn":[17],"good":[19,37],"and":[20,167,228],"adaptive":[21],"prior":[23,51],"over":[24,52,134],"small":[25],"patches.":[27],"However,":[28],"expected":[29],"log":[31],"ikelihood":[32],"(EPLL)":[33],"framework":[34,97],"achieved":[35],"performance":[38],"applications,":[42],"it":[43],"mainly":[44],"employs":[45],"the":[46,66,69,75,95,128,135,139,146,171,190,195,203,213],"Gaussian":[47],"Mixture":[48],"model":[49,149],"(GMM)":[50],"large":[54],"number":[55],"of":[56,138,148,198],"training":[57],"patches":[58,182,215],"which":[59,80,142,183],"gains":[60],"huge":[62],"computational":[63],"cost.":[64],"On":[65],"other":[67],"hand,":[68],"GMM":[70],"parameters":[71,131],"are":[72],"learned":[73],"with":[74,117,153],"Expectation":[76],"Maximization":[77],"(EM)":[78,140],"algorithm":[79,141,176],"usually":[81],"suffers":[82],"from":[83],"local":[84,191],"optimum":[85],"problem.":[86],"This":[87],"paper":[88],"proposes":[89],"post":[91],"filter":[92],"by":[93,164,217],"extending":[94],"EPLL":[96],"reduce":[99],"noise":[100],"CT":[102],"images":[103],"acquired":[104],"on":[105,145],"low":[106],"radiation":[107],"dose,":[108],"order":[110],"get":[112],"better":[114,225],"quality":[115],"fine":[118],"details":[119],"preserved.":[120],"basic":[122],"incrementally":[126],"estimate":[127],"mixture":[129,162],"component":[130],"skip":[133],"significant":[136],"drawback":[137],"greatly":[143],"depends":[144],"initialization":[147],"parameters.":[150],"Incremental":[151],"method":[152,222],"split-merge":[154],"mixtures":[155],"learning":[156,161],"(SMILE)":[157],"utilized":[159],"for":[160],"models":[163],"consecutive":[165],"split":[166],"merge":[168],"operations.":[169],"Then,":[170],"Ant":[172],"Colony":[173],"Optimization":[174],"(ACO)":[175],"used":[178],"select":[180],"homogeneous":[181,214],"tend":[184],"cluster":[186],"together":[187],"due":[188],"spatial":[192],"smoothness":[193],"image.":[196],"Instead":[197],"restoring":[199],"each":[200],"separately,":[202],"maximum":[204],"posteriori":[206],"(MAP)":[207],"estimation":[208],"employed":[210],"denoise":[212],"selected":[216],"ACO":[218],"algorithm.":[219],"proposed":[221],"leads":[223],"clustering":[226],"results":[227],"superior":[229],"denoised":[230],"results.":[231]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}