{"id":"https://openalex.org/W4403534648","doi":"https://doi.org/10.1109/codit62066.2024.10708626","title":"Exploring Convolutional Autoencoder Efficacy in Noise Removal for Image Processing and Computer Vision: A Study Using the MNIST Dataset","display_name":"Exploring Convolutional Autoencoder Efficacy in Noise Removal for Image Processing and Computer Vision: A Study Using the MNIST Dataset","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W4403534648","doi":"https://doi.org/10.1109/codit62066.2024.10708626"},"language":"en","primary_location":{"id":"doi:10.1109/codit62066.2024.10708626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit62066.2024.10708626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5093644965","display_name":"Elma Kandi\u0107","orcid":"https://orcid.org/0009-0006-4722-7140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elma Kandi\u0107","raw_affiliation_strings":["University of P&#x00E9;cs,Faculty of Engineering and Information Technology,P&#x00E9;cs,Hungary,7624"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of P&#x00E9;cs,Faculty of Engineering and Information Technology,P&#x00E9;cs,Hungary,7624","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055747675","display_name":"Amila Akagi\u0107","orcid":"https://orcid.org/0000-0002-4795-5424"},"institutions":[{"id":"https://openalex.org/I104817121","display_name":"University of Sarajevo","ror":"https://ror.org/02hhwgd43","country_code":"BA","type":"education","lineage":["https://openalex.org/I104817121"]}],"countries":["BA"],"is_corresponding":false,"raw_author_name":"Amila Akagic","raw_affiliation_strings":["University of Sarajevo, Zmaja od Bosne bb, University Campus,Department of Computer Science and Informatics,Sarajevo,Bosnia and Herzegovina,71000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sarajevo, Zmaja od Bosne bb, University Campus,Department of Computer Science and Informatics,Sarajevo,Bosnia and Herzegovina,71000","institution_ids":["https://openalex.org/I104817121"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056303178","display_name":"Mahdi Bohlouli","orcid":"https://orcid.org/0000-0002-6659-5524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahdi Bohlouli","raw_affiliation_strings":["Petanux GmbH,Research and Innovation Department,Bonn,Germany,D-53129"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petanux GmbH,Research and Innovation Department,Bonn,Germany,D-53129","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1275","last_page":"1280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9399999976158142,"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":0.9399999976158142,"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/mnist-database","display_name":"MNIST database","score":0.9146693348884583},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8335384130477905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7251400351524353},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6922465562820435},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6345333456993103},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5974576473236084},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.5799654722213745},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5172277688980103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48876309394836426},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44339117407798767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.23243707418441772}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9146693348884583},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8335384130477905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7251400351524353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6922465562820435},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6345333456993103},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5974576473236084},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.5799654722213745},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5172277688980103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48876309394836426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44339117407798767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.23243707418441772}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit62066.2024.10708626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit62066.2024.10708626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1484309825","https://openalex.org/W1541168159","https://openalex.org/W1575740594","https://openalex.org/W1661703005","https://openalex.org/W1990881969","https://openalex.org/W2007339694","https://openalex.org/W2038103816","https://openalex.org/W2059784307","https://openalex.org/W2103559027","https://openalex.org/W2137969878","https://openalex.org/W2143131425","https://openalex.org/W2157181469","https://openalex.org/W2159736423","https://openalex.org/W2167754205","https://openalex.org/W2310913313","https://openalex.org/W2333126212","https://openalex.org/W2408469841","https://openalex.org/W2409877064","https://openalex.org/W2510850936","https://openalex.org/W2769995481","https://openalex.org/W2784144374","https://openalex.org/W2786003328","https://openalex.org/W2820727372","https://openalex.org/W2919234133","https://openalex.org/W3086712991","https://openalex.org/W3184998487","https://openalex.org/W3208501915","https://openalex.org/W4246922995","https://openalex.org/W4385958045","https://openalex.org/W6892710045","https://openalex.org/W7038994942"],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W3036048022","https://openalex.org/W4309224979"],"abstract_inverted_index":{"Noise":[0],"removal":[1,113],"in":[2,25,46,72,151,174,180],"image":[3,153],"processing":[4],"and":[5,63,87,103,128,170],"computer":[6],"vision":[7],"is":[8,114],"a":[9,14],"crucial":[10,57],"preprocessing":[11,55],"step":[12],"employing":[13],"spectrum":[15],"of":[16,70,100,105,109,119,143,148,159,172,177],"techniques.":[17],"In":[18,130],"recent":[19],"years,":[20],"autoencoders":[21,71,110,173],"exhibit":[22],"remarkable":[23,132],"efficacy":[24,108,147],"mapping":[26],"noisy":[27],"images":[28,78],"to":[29],"clean":[30],"counterparts,":[31],"capturing":[32],"intricate":[33],"relationships":[34],"for":[35,58,111],"effective":[36],"noise":[37,45,76,112,178],"removal.":[38],"Motivated":[39],"by":[40,44],"the":[41,53,68,80,98,117,134,146,149,156,168,175],"challenges":[42],"posed":[43],"real-world":[47],"images,":[48],"this":[49],"research":[50],"focuses":[51],"on":[52,93],"denoising":[54],"step,":[56],"tasks":[59],"like":[60],"object":[61],"detection":[62],"segmentation.":[64],"The":[65,83,107],"study":[66],"explores":[67],"application":[69],"removing":[73],"artificially":[74],"added":[75],"from":[77],"within":[79],"MNIST":[81,84],"dataset.":[82],"dataset\u2019s":[85],"simplicity":[86],"historical":[88],"significance":[89],"facilitate":[90],"focused":[91],"examinations":[92],"specific":[94],"aspects,":[95],"such":[96],"as":[97],"impact":[99],"different":[101],"types":[102],"levels":[104],"noise.":[106,160],"assessed":[115],"through":[116],"evaluation":[118],"results":[120],"using":[121],"various":[122,181],"metrics,":[123],"including":[124],"SSIM,":[125],"PSNR,":[126],"MSE,":[127],"RMSE.":[129],"one":[131],"instance,":[133],"reconstruction":[135],"process":[136],"achieved":[137],"an":[138],"impressive":[139],"peak":[140],"SSIM":[141],"score":[142],"99.06%,":[144],"showcasing":[145],"method":[150],"preserving":[152],"fidelity":[154],"despite":[155],"challenging":[157],"presence":[158],"This":[161],"comprehensive":[162],"analysis":[163],"provides":[164],"valuable":[165],"insights":[166],"into":[167],"performance":[169],"effectiveness":[171],"context":[176],"reduction":[179],"domains.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
