{"id":"https://openalex.org/W7171932222","doi":"https://doi.org/10.1145/3774521.3774592","title":"IDANet: Identity-Driven Attention Network for Unsupervised Synthetic Noise Removal","display_name":"IDANet: Identity-Driven Attention Network for Unsupervised Synthetic Noise Removal","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7171932222","doi":"https://doi.org/10.1145/3774521.3774592"},"language":null,"primary_location":{"id":"doi:10.1145/3774521.3774592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774592","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774521.3774592","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5144177420","display_name":"Akhilaraj D","orcid":"https://orcid.org/0000-0002-2202-6100"},"institutions":[{"id":"https://openalex.org/I3129367976","display_name":"APJ Abdul Kalam Technological University","ror":"https://ror.org/04yn30r61","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129367976"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akhilaraj D","raw_affiliation_strings":["Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India and APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India"],"raw_orcid":"https://orcid.org/0000-0002-2202-6100","affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India and APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India","institution_ids":["https://openalex.org/I3129367976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048958651","display_name":"Joseph Zacharias","orcid":"https://orcid.org/0000-0001-5816-6570"},"institutions":[{"id":"https://openalex.org/I3129367976","display_name":"APJ Abdul Kalam Technological University","ror":"https://ror.org/04yn30r61","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129367976"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Joseph Zacharias","raw_affiliation_strings":["Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India and APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India"],"raw_orcid":"https://orcid.org/0000-0001-5816-6570","affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India and APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India","institution_ids":["https://openalex.org/I3129367976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3129367976"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6370000243186951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6133000254631042},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5935999751091003},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5795999765396118},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5454000234603882},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.487199991941452},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46790000796318054},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44020000100135803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480999827384949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873999834060669},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6370000243186951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6133000254631042},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5795999765396118},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5454000234603882},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2603999972343445},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774521.3774592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774592","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774521.3774592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774592","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2026019078","https://openalex.org/W2048695508","https://openalex.org/W2133665775","https://openalex.org/W2151707108","https://openalex.org/W2194775991","https://openalex.org/W2508457857","https://openalex.org/W2607041014","https://openalex.org/W2613155248","https://openalex.org/W2752782242","https://openalex.org/W2764207251","https://openalex.org/W2799192307","https://openalex.org/W2820727372","https://openalex.org/W2884585870","https://openalex.org/W2902857081","https://openalex.org/W2914992179","https://openalex.org/W2962767526","https://openalex.org/W2963315679","https://openalex.org/W2963725279","https://openalex.org/W2966412451","https://openalex.org/W2971719842","https://openalex.org/W2983315964","https://openalex.org/W2995224431","https://openalex.org/W3034504121","https://openalex.org/W3035326127","https://openalex.org/W3035383808","https://openalex.org/W3035542568","https://openalex.org/W3090412929","https://openalex.org/W3099686304","https://openalex.org/W3106758205","https://openalex.org/W3129492101","https://openalex.org/W3133953507","https://openalex.org/W3157284486","https://openalex.org/W3162080183","https://openalex.org/W3170697543","https://openalex.org/W3174300208","https://openalex.org/W3193957345","https://openalex.org/W3195697494","https://openalex.org/W4225672218","https://openalex.org/W4283310858","https://openalex.org/W4312812783","https://openalex.org/W4316662319","https://openalex.org/W4364356857","https://openalex.org/W4364379239","https://openalex.org/W4376130200","https://openalex.org/W4390990495","https://openalex.org/W4411373503"],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"image":[1,170],"denoising":[2],"remains":[3],"a":[4,93,132,163],"challenging":[5],"task":[6],"due":[7],"to":[8,101],"the":[9,41,53,82,120,123,127,153],"heterogeneous,":[10],"signal-dependent,":[11],"and":[12,52,77,89,117,136,147,158,165],"often":[13],"unpredictable":[14],"nature":[15],"of":[16,122,134,139,155],"noise.":[17],"This":[18],"paper":[19],"introduces":[20],"IDANet,":[21],"an":[22,137],"identity-driven":[23,156],"attention":[24],"network":[25],"developed":[26],"for":[27,168],"unsupervised":[28],"synthetic":[29],"noise":[30],"removal.":[31],"The":[32],"proposed":[33,124],"architecture":[34],"integrates":[35],"two":[36],"complementary":[37],"feature":[38,160],"extraction":[39],"branches:":[40],"Adaptive":[42],"Contextual":[43],"Residual":[44],"Unit":[45,57],"(ACRU),":[46],"which":[47,59],"models":[48],"adaptive":[49],"local":[50],"context,":[51],"Multi-Scale":[54],"Dilated":[55],"Fusion":[56],"(MDFU),":[58],"captures":[60],"long-range":[61],"dependencies.":[62],"These":[63,149],"branches":[64],"are":[65],"interconnected":[66],"via":[67],"Flow-through":[68],"Identity":[69],"Units":[70],"(FIUs)":[71],"that":[72,152],"enable":[73],"stable":[74],"gradient":[75],"propagation":[76],"preserve":[78],"spatial":[79],"structure":[80],"throughout":[81],"network.":[83],"To":[84],"further":[85],"enhance":[86],"contextual":[87],"aggregation":[88],"maintain":[90],"structural":[91],"integrity,":[92],"Feature":[94],"Aggregation":[95],"Refinement":[96],"(FAR)":[97],"module":[98],"is":[99],"incorporated":[100],"adaptively":[102],"fuse":[103],"multi-level":[104],"features":[105],"while":[106],"ensuring":[107],"identity":[108],"consistency.":[109],"Extensive":[110],"experiments":[111],"on":[112],"standard":[113],"benchmarks,":[114],"including":[115],"SIDD":[116],"DND,":[118],"demonstrate":[119],"effectiveness":[121],"approach.":[125],"On":[126],"DND":[128],"dataset,":[129],"IDANet":[130],"achieves":[131],"PSNR":[133],"39.75dB":[135],"SSIM":[138],"0.9538,":[140],"surpassing":[141],"state-of-the-art":[142],"methods":[143],"such":[144],"as":[145],"CycleISP":[146],"SADNet.":[148],"results":[150],"validate":[151],"combination":[154],"connectivity":[157],"attention-guided":[159],"fusion":[161],"provides":[162],"robust":[164],"efficient":[166],"framework":[167],"real-world":[169],"denoising.":[171]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-08-01T00:00:00"}
