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dc.contributor.author | Almutairi, Khleef![]() |
es_ES |
dc.contributor.author | Morillas, Samuel![]() |
es_ES |
dc.contributor.author | Latorre-Carmona, Pedro![]() |
es_ES |
dc.date.accessioned | 2024-07-22T18:05:49Z | |
dc.date.available | 2024-07-22T18:05:49Z | |
dc.date.issued | 2024-03 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/206525 | |
dc.description.abstract | [EN] Image denoising is a fundamental research topic in colour image processing, analysis, and transmission. Noise is an inevitable byproduct of image acquisition and transmission, and its nature is intimately linked to the underlying processes that produce it. Gaussian noise is a particularly prevalent type of noise that necessitates effective removal while ensuring the preservation of the original image's quality. This paper presents a colour image denoising framework that integrates fuzzy inference systems (FISs) with eigenvector analysis. This framework employs eigenvector analysis to extract relevant information from local image neighbourhoods. This information is subsequently fed into the FIS system which dynamically adjusts the intensity of the denoising process based on local characteristics. This approach recognizes that homogeneous areas may require less aggressive smoothing than detailed image regions. Images are converted from the RGB domain to an eigenvector-based space for smoothing and then converted back to the RGB domain. The effectiveness of the proposed methods is established through the application of various image quality metrics and visual comparisons against established state-of-the-art techniques. | es_ES |
dc.description.sponsorship | This research was funded by Generalitat Valenciana under grant CIAICO/2022-051 IMaLeVICS and Spanish Ministry of Science under grant PID2022-140189OB-C21. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Electronics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Colour image processing | es_ES |
dc.subject | Fuzzy inference system | es_ES |
dc.subject | Eigenvector analysis | es_ES |
dc.subject | Gaussian noise | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/electronics13061150 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-140189OB-C21/ES/RECUPERACION DE IMAGENES BASADA EN EL CONTENIDO PARA EL DIAGNOSTICO DE TUMORES CUTANEOS PRIMARIOS Y SECUNDARIOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//CIAICO%2F2022%2F051/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Almutairi, K.; Morillas, S.; Latorre-Carmona, P. (2024). Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method. Electronics. 13(6). https://doi.org/10.3390/electronics13061150 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/electronics13061150 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 13 | es_ES |
dc.description.issue | 6 | es_ES |
dc.identifier.eissn | 2079-9292 | es_ES |
dc.relation.pasarela | S\520512 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |