{"id":"https://openalex.org/W4409537066","doi":"https://doi.org/10.1109/icvisp64524.2024.10959459","title":"Efficient and Fast Self-Supervised Image Denoising Based on Cyclic Multi-Variate Function","display_name":"Efficient and Fast Self-Supervised Image Denoising Based on Cyclic Multi-Variate Function","publication_year":2024,"publication_date":"2024-12-27","ids":{"openalex":"https://openalex.org/W4409537066","doi":"https://doi.org/10.1109/icvisp64524.2024.10959459"},"language":"en","primary_location":{"id":"doi:10.1109/icvisp64524.2024.10959459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959459","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","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/A5056463512","display_name":"Xu Liu","orcid":"https://orcid.org/0000-0002-6766-5184"},"institutions":[{"id":"https://openalex.org/I120825670","display_name":"Yunnan Normal University","ror":"https://ror.org/00sc9n023","country_code":"CN","type":"education","lineage":["https://openalex.org/I120825670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xu Liu","raw_affiliation_strings":["School of Information Science and Technology, Yunnan Normal University,Kunming,China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Yunnan Normal University,Kunming,China","institution_ids":["https://openalex.org/I120825670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103086350","display_name":"Yungang Zhang","orcid":"https://orcid.org/0000-0002-8279-3109"},"institutions":[{"id":"https://openalex.org/I120825670","display_name":"Yunnan Normal University","ror":"https://ror.org/00sc9n023","country_code":"CN","type":"education","lineage":["https://openalex.org/I120825670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yungang Zhang","raw_affiliation_strings":["School of Information Science and Technology, Yunnan Normal University,Kunming,China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Yunnan Normal University,Kunming,China","institution_ids":["https://openalex.org/I120825670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056463512"],"corresponding_institution_ids":["https://openalex.org/I120825670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30707141,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9753999710083008,"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":0.9753999710083008,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9513000249862671,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.920799970626831,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-variate","display_name":"Random variate","score":0.773943305015564},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.702613115310669},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6221297979354858},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6043991446495056},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5901085734367371},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5148274302482605},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5095413327217102},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4705759882926941},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4143771231174469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2443142533302307},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13729751110076904},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.07860541343688965}],"concepts":[{"id":"https://openalex.org/C141547133","wikidata":"https://www.wikidata.org/wiki/Q7291996","display_name":"Random variate","level":3,"score":0.773943305015564},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.702613115310669},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6221297979354858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6043991446495056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5901085734367371},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5148274302482605},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5095413327217102},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4705759882926941},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4143771231174469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2443142533302307},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13729751110076904},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.07860541343688965},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp64524.2024.10959459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959459","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1906770428","https://openalex.org/W2037642501","https://openalex.org/W2508457857","https://openalex.org/W2752782242","https://openalex.org/W2764207251","https://openalex.org/W2799192307","https://openalex.org/W2902857081","https://openalex.org/W2963494934","https://openalex.org/W2963725279","https://openalex.org/W2964125708","https://openalex.org/W2983315964","https://openalex.org/W2997508111","https://openalex.org/W3034552520","https://openalex.org/W3097262261","https://openalex.org/W3169628275","https://openalex.org/W3174829522","https://openalex.org/W3178192988","https://openalex.org/W4224104371","https://openalex.org/W4312444885","https://openalex.org/W4312575110","https://openalex.org/W4312624488","https://openalex.org/W4320489866","https://openalex.org/W4385804839","https://openalex.org/W4386047745","https://openalex.org/W6759001153","https://openalex.org/W6762734363","https://openalex.org/W6784847106"],"related_works":["https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W4407169614","https://openalex.org/W2387428419","https://openalex.org/W4401571043","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W4406054402","https://openalex.org/W4312627788","https://openalex.org/W2353444452"],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,9,173],"neural":[3],"networks":[4],"have":[5,42],"driven":[6],"notable":[7,71],"progress":[8],"image":[10,120],"denoising.":[11],"Nevertheless,":[12],"conventional":[13],"supervised":[14],"approaches":[15],"rely":[16],"heavily":[17],"on":[18,38,57,208],"large":[19],"datasets":[20],"of":[21,119,134,157,198],"paired":[22],"noisy":[23,40,125],"and":[24,55,85,100,112,147,164,188,201],"clean":[25],"images,":[26],"which":[27,152],"restricts":[28],"their":[29],"practicality.":[30],"Meanwhile,":[31],"self-supervised":[32],"denoising":[33,121,191],"methods":[34,159],"that":[35,106],"train":[36],"directly":[37],"single":[39],"images":[41],"been":[43],"developed":[44],"but":[45,77],"face":[46],"limitations":[47],"such":[48],"as":[49],"inefficiency":[50],"during":[51],"training,":[52],"information":[53],"loss,":[54],"reliance":[56],"noise":[58],"modeling.":[59],"The":[60,127],"Cyclic":[61,102],"Multi-Variate":[62,103],"Function":[63,104],"for":[64],"Self-Supervised":[65],"Image":[66],"Denoising":[67],"(CVF-SID)":[68],"method":[69,200],"offers":[70],"improvements":[72,129],"to":[73,96,160],"address":[74],"these":[75],"problems":[76],"still":[78],"faces":[79],"challenges":[80],"with":[81,141,175],"high":[82],"resource":[83,110],"consumption":[84,111],"long":[86],"training":[87,118,186],"cycles.":[88],"In":[89],"this":[90],"paper,":[91],"we":[92,143],"present":[93],"an":[94],"improvement":[95],"the":[97,132,145,155,176,199],"CVF-SID":[98,178],"method\u2014Efficient":[99],"Fast":[101],"(EF-CVF)":[105],"significantly":[107],"reduces":[108,181],"GPU":[109,182],"accelerates":[113],"convergence,":[114],"enabling":[115],"more":[116],"efficient":[117],"models":[122],"using":[123],"only":[124],"images.":[126],"key":[128],"include":[130],"adjusting":[131],"number":[133],"convolutional":[135],"layers,":[136],"Combining":[137],"Efficient":[138],"Channel":[139,148],"Attention":[140,149],"Squeeze-and-Excitation,":[142],"designed":[144],"Squeeze-and-Excitation":[146],"(SECA)":[150],"mechanism,":[151],"effectively":[153,180],"integrates":[154],"strengths":[156],"both":[158],"enhance":[161],"feature":[162],"representation.,":[163],"introducing":[165],"a":[166,195],"Partial":[167],"Convolution":[168],"(PConv)":[169],"module.":[170],"Our":[171],"method,":[172,179],"comparison":[174],"original":[177],"load,":[183],"improves":[184],"model":[185],"speed,":[187],"achieves":[189],"enhanced":[190],"performance.":[192],"We":[193],"provide":[194],"theoretical":[196],"explanation":[197],"validate":[202],"its":[203],"effectiveness":[204],"through":[205],"extensive":[206],"experiments":[207],"real-world":[209],"datasets.":[210]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}