{"id":"https://openalex.org/W7131287139","doi":"https://doi.org/10.1109/vcip67698.2025.11396813","title":"Fusion Networks for Advanced Block-Overlapping SMoE Image Regression","display_name":"Fusion Networks for Advanced Block-Overlapping SMoE Image Regression","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7131287139","doi":"https://doi.org/10.1109/vcip67698.2025.11396813"},"language":null,"primary_location":{"id":"doi:10.1109/vcip67698.2025.11396813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","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/A5126668845","display_name":"Na Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Na Li","raw_affiliation_strings":["Technische Universität Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Universität Berlin","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053803378","display_name":"T. Sikora","orcid":"https://orcid.org/0009-0009-2721-6796"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Sikora","raw_affiliation_strings":["Technische Universität Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Universität Berlin","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.761928,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.18459999561309814,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.18459999561309814,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.07660000026226044,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.05810000002384186,"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/regression","display_name":"Regression","score":0.4909000098705292},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.48840001225471497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46959999203681946},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.46000000834465027},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.45660001039505005},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.42250001430511475},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4207000136375427},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.38440001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6675000190734863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6448000073432922},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4909000098705292},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4207000136375427},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35190001130104065},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.3140999972820282},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3050000071525574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip67698.2025.11396813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6117665767669678}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2006262236","https://openalex.org/W2056370875","https://openalex.org/W2133176659","https://openalex.org/W2159736423","https://openalex.org/W2667792218","https://openalex.org/W2808611867","https://openalex.org/W2890711913","https://openalex.org/W2892310177","https://openalex.org/W2944781240","https://openalex.org/W2945734389","https://openalex.org/W2952421489","https://openalex.org/W2965252765","https://openalex.org/W3037582100","https://openalex.org/W3049688872","https://openalex.org/W3169893408","https://openalex.org/W3215769467","https://openalex.org/W4292382515","https://openalex.org/W4379530952","https://openalex.org/W4380684018","https://openalex.org/W4384947635","https://openalex.org/W4386494448","https://openalex.org/W4386724312","https://openalex.org/W4388118937","https://openalex.org/W4391247671","https://openalex.org/W4393248012","https://openalex.org/W4399852930"],"related_works":[],"abstract_inverted_index":{"Our":[0],"challenge":[1],"is":[2,26,75,120],"to":[3,20,47,150],"develop":[4],"efficient":[5],"block-overlapping":[6,37],"regression":[7,38,56,145],"strategies":[8,135],"for":[9,28,51,59,86],"clean":[10,65,94],"and":[11,79,83,136,146,164,169],"noisy":[12,67,116],"images.":[13],"Block-based":[14],"approaches":[15,159],"allow":[16],"massive":[17],"parallel":[18],"computation":[19],"achieve":[21],"fast":[22],"rendering":[23],"-":[24,69,124],"which":[25,44],"important":[27],"many":[29],"applications.":[30,53],"In":[31],"this":[32],"paper":[33],"we":[34],"focus":[35],"on":[36,153],"with":[39,109,125],"sparse":[40],"Steered-Mixture-of-Experts":[41],"(SMoE)":[42],"models":[43],"have":[45,147],"shown":[46],"provide":[48],"excellent":[49],"results":[50,127,137],"divers":[52],"Block-overlapping":[54],"SMoE":[55,144],"provides":[57],"multi-hypotheses":[58],"estimating":[60],"each":[61],"pixel":[62],"value":[63],"from":[64],"or":[66],"observations":[68,117],"usually":[70],"simple":[71],"averaging":[72],"of":[73,139,156],"hypotheses":[74],"performed.":[76],"We":[77],"introduce":[78],"investigate":[80],"several":[81],"linear":[82],"nonlinear":[84],"Filters/Networks":[85],"fusing":[87],"the":[88,93,106,118,129,148,154],"estimates":[89],"more":[90,140],"efficiently.":[91],"On":[92],"Kodak":[95],"image":[96],"test":[97],"set":[98],"our":[99],"best":[100],"Fusion":[101],"Network":[102],"can":[103],"improve":[104,151],"over":[105],"\"average\"":[107],"filter":[108],"an":[110],"impressive":[111],"4.6-5.7":[112],"dB":[113,123],"gain.":[114],"For":[115],"gain":[119],"around":[121],"1.6":[122],"denoising":[126],"in":[128,167],"same":[130],"ball-park":[131],"as":[132,161],"BM3D.The":[133],"developed":[134],"appear":[138],"general":[141],"interest":[142],"beyond":[143],"potential":[149],"also":[152],"plethora":[155],"other":[157],"block-regression":[158],"such":[160],"DNN":[162],"Autoencoders":[163],"kernel":[165],"methods":[166],"2D":[168],"3D":[170],"Gaussian":[171],"Splatting":[172]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-25T00:00:00"}