2026-05-04T15:35:09Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/112322026-01-17T01:05:28Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378
Repositorio Institucional de la Universidad de Burgos
author
Almutairi, Khleef
author
Morillas Gómez, Samuel
author
Latorre Carmona, Pedro
2026-01-16T12:19:28Z
2026-01-16T12:19:28Z
2024-03
https://hdl.handle.net/10259/11232
10.3390/electronics13061150
2079-9292
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.
eng
Atribución 4.0 Internacional
Colour image processing
Fuzzy inference system
Eigenvector analysis
Gaussian noise
Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/11232/1/Almutairi-electronics_2024.pdf
File
MD5
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application/pdf
Almutairi-electronics_2024.pdf