{"id":"https://openalex.org/W4409084306","doi":"https://doi.org/10.32604/cmc.2025.060252","title":"Frequency-Quantized Variational Autoencoder Based on 2D-FFT for Enhanced Image Reconstruction and Generation","display_name":"Frequency-Quantized Variational Autoencoder Based on 2D-FFT for Enhanced Image Reconstruction and Generation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409084306","doi":"https://doi.org/10.32604/cmc.2025.060252"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.060252","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.060252","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.060252","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103183672","display_name":"Jianxin Feng","orcid":"https://orcid.org/0009-0007-6866-9252"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jianxin Feng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058003036","display_name":"Xiaoyao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaoyao Liu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103183672"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2565,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79264382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"83","issue":"2","first_page":"2087","last_page":"2107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9528999924659729,"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"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9528999924659729,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.949400007724762,"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/autoencoder","display_name":"Autoencoder","score":0.9035382270812988},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.779266357421875},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6000164747238159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42701634764671326},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4195792078971863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4190511703491211},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40606018900871277},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3665943741798401},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3465263843536377},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15466392040252686}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9035382270812988},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.779266357421875},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6000164747238159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42701634764671326},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4195792078971863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4190511703491211},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40606018900871277},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3665943741798401},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3465263843536377},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15466392040252686}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.060252","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.060252","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.060252","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.060252","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W3047443805","https://openalex.org/W3096831136","https://openalex.org/W3162090017","https://openalex.org/W4212852978","https://openalex.org/W4321113904","https://openalex.org/W4321381231","https://openalex.org/W4321608115","https://openalex.org/W4362496454","https://openalex.org/W4362668196","https://openalex.org/W4363650848","https://openalex.org/W4386568617","https://openalex.org/W4387675711","https://openalex.org/W4415796345"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866"],"abstract_inverted_index":{"As":[0],"a":[1,57,81,125],"form":[2],"of":[3,25,119,154],"discrete":[4],"representation":[5],"learning,":[6],"Vector":[7],"Quantized":[8],"Variational":[9],"Autoencoders":[10],"(VQ-VAE)":[11],"have":[12],"increasingly":[13],"been":[14],"applied":[15],"to":[16,22,63,95,105,133,179],"generative":[17],"and":[18,27,44,50,87,108,117,165,194],"multimodal":[19],"tasks":[20],"due":[21],"their":[23],"ease":[24],"embedding":[26],"representative":[28],"capacity.":[29],"However,":[30],"existing":[31],"VQ-VAEs":[32],"often":[33],"perform":[34],"quantization":[35,90],"in":[36,76,152],"the":[37,77,114,146,172,175,180,184,188],"spatial":[38],"domain,":[39],"ignoring":[40],"global":[41,98],"structural":[42],"information":[43,51,109],"potentially":[45],"suffering":[46],"from":[47],"codebook":[48,101],"collapse":[49,107],"coupling":[52,110],"issues.":[53,66],"This":[54],"paper":[55],"proposes":[56],"frequency":[58,78,93,116],"quantized":[59],"variational":[60],"autoencoder":[61],"(FQ-VAE)":[62],"address":[64],"these":[65,92],"The":[67,100],"proposed":[68,147,185],"method":[69,148,182,186],"transforms":[70],"image":[71],"features":[72],"into":[73],"linear":[74],"combinations":[75],"domain":[79],"using":[80],"2D":[82],"fast":[83],"Fourier":[84],"transform":[85],"(2D-FFT)":[86],"performs":[88],"adaptive":[89],"on":[91,129,140,174],"components":[94],"preserve":[96],"image\u2019s":[97],"relationships.":[99],"is":[102],"dynamically":[103],"optimized":[104],"avoid":[106],"issue":[111],"by":[112,191],"considering":[113],"usage":[115],"dependency":[118],"code":[120],"vectors.":[121],"Furthermore,":[122],"we":[123],"introduce":[124],"post-processing":[126],"module":[127],"based":[128],"graph":[130],"convolutional":[131],"networks":[132],"further":[134],"improve":[135],"reconstruction":[136],"quality.":[137],"Experimental":[138],"results":[139],"four":[141],"public":[142],"datasets":[143],"demonstrate":[144],"that":[145],"outperforms":[149],"state-of-the-art":[150],"approaches":[151],"terms":[153],"Structural":[155],"Similarity":[156,163],"Index":[157],"(SSIM),":[158],"Learned":[159],"Perceptual":[160],"Image":[161],"Patch":[162],"(LPIPS),":[164],"Reconstruction":[166],"Fr\u00e9chet":[167],"Inception":[168],"Distance":[169],"(rFID).":[170],"In":[171],"experiments":[173],"CIFAR-10":[176],"dataset,":[177],"compared":[178],"baseline":[181],"VQ-VAE,":[183],"improves":[187],"above":[189],"metrics":[190],"4.9%,":[192],"36.4%,":[193],"52.8%,":[195],"respectively.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
