{"id":"https://openalex.org/W4388938138","doi":"https://doi.org/10.1109/icccnt56998.2023.10307208","title":"An Evaluation of Denoising Methods for Satellite Imagery: A Comparative Analysis","display_name":"An Evaluation of Denoising Methods for Satellite Imagery: A Comparative Analysis","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388938138","doi":"https://doi.org/10.1109/icccnt56998.2023.10307208"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10307208","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5040026160","display_name":"Malavika Jayakumar","orcid":"https://orcid.org/0000-0003-4605-0030"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Malavika Jayakumar","raw_affiliation_strings":["Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093324406","display_name":"Rahul Gudivada","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul Gudivada","raw_affiliation_strings":["Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113052835","display_name":"Harsh Nair","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Harsh Nair","raw_affiliation_strings":["Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041776287","display_name":"S. Jenicka","orcid":"https://orcid.org/0000-0001-8975-8712"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S. Jenicka","raw_affiliation_strings":["Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology (VIT),School of Computer Science and Engineering (SCOPE),Vellore","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040026160"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15426723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9986000061035156,"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":0.9912999868392944,"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/computer-science","display_name":"Computer science","score":0.7469344139099121},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7308427095413208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6843699216842651},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6212714314460754},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5534060597419739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5144534111022949},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4902339577674866},{"id":"https://openalex.org/keywords/peak-signal-to-noise-ratio","display_name":"Peak signal-to-noise ratio","score":0.4852862060070038},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4616056978702545},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4479387402534485},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.23406538367271423},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21044349670410156},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1493758261203766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7469344139099121},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7308427095413208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6843699216842651},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6212714314460754},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5534060597419739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5144534111022949},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4902339577674866},{"id":"https://openalex.org/C154579607","wikidata":"https://www.wikidata.org/wiki/Q3373850","display_name":"Peak signal-to-noise ratio","level":3,"score":0.4852862060070038},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4616056978702545},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4479387402534485},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.23406538367271423},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21044349670410156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1493758261203766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10307208","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6899999976158142,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1546461108","https://openalex.org/W2508457857","https://openalex.org/W2752782242","https://openalex.org/W2800563186","https://openalex.org/W2902519853","https://openalex.org/W2941036984","https://openalex.org/W2945177640","https://openalex.org/W2963966343","https://openalex.org/W2965060898","https://openalex.org/W2968493457","https://openalex.org/W3156260480","https://openalex.org/W4318822211","https://openalex.org/W6632612237"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2315873829","https://openalex.org/W2883512610"],"abstract_inverted_index":{"Satellite":[0],"images":[1],"often":[2],"suffer":[3],"from":[4],"noise,":[5],"which":[6],"hampers":[7],"data":[8],"reliability":[9],"and":[10,51,65,96,117,141,174,198],"analysis":[11],"accuracy.":[12],"This":[13,40,178],"study":[14,41,179],"presents":[15],"a":[16,26,30,43,104],"comparative":[17],"evaluation":[18,84],"of":[19,45,77,149,158,195],"denoising":[20,81,143,168,202],"algorithms":[21,152],"for":[22,186],"satellite":[23,190,203],"imagery,":[24,191],"with":[25,69],"specific":[27],"focus":[28],"on":[29,108,183],"deep":[31,150,196],"learning":[32,197],"method":[33],"called":[34],"Deep":[35],"Convolutional":[36],"Neural":[37],"Network":[38],"(DnCNN).":[39],"includes":[42],"comparison":[44],"spatial":[46],"filters":[47,57],"such":[48,58,86],"as":[49,59,87],"NLM,":[50],"weighted":[52],"mean":[53,97],"filters,":[54,61,64],"frequency":[55],"domain":[56],"gaussian":[60],"wavelet":[62],"transform-based":[63],"the":[66,75,109,133,147,162,167,193],"DnCNN":[67,110,134,163],"model":[68,164],"an":[70],"attention":[71,118,159,199],"module.":[72],"To":[73],"assess":[74],"effectiveness":[76],"these":[78],"techniques":[79],"in":[80,189,201],"images,":[82],"various":[83],"metrics":[85],"peak":[88],"signal-to-noise":[89],"ratio":[90],"(PSNR),":[91],"structural":[92],"similarity":[93],"index":[94],"(SSIM),":[95],"squared":[98],"error":[99],"(MSE)":[100],"are":[101],"utilized.":[102],"Moreover,":[103],"novel":[105],"approach":[106],"based":[107],"network":[111],"is":[112],"presented,":[113],"leveraging":[114,171],"multiple":[115],"layers":[116],"modules":[119,160],"to":[120,137],"selectively":[121],"enhance":[122],"important":[123],"image":[124,172],"features":[125],"while":[126],"suppressing":[127],"noise.":[128],"The":[129,156],"proposed":[130],"methodology":[131],"highlights":[132],"network's":[135],"ability":[136],"capture":[138],"intricate":[139],"details":[140],"improve":[142],"performance.":[144],"Results":[145],"demonstrate":[146],"superiority":[148],"learning-based":[151],"over":[153],"alternative":[154],"methods.":[155],"integration":[157],"within":[161],"further":[165],"enhances":[166],"process":[169],"by":[170],"correlations":[173],"extracting":[175],"pertinent":[176],"information.":[177],"offers":[180],"valuable":[181],"findings":[182],"effective":[184],"methods":[185],"reducing":[187],"noise":[188],"highlighting":[192],"potential":[194],"mechanisms":[200],"images.":[204]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}