RT info:eu-repo/semantics/article
T1 Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method
A1 Almutairi, Khleef
A1 Morillas Gómez, Samuel
A1 Latorre Carmona, Pedro
K1 Colour image processing
K1 Fuzzy inference system
K1 Eigenvector analysis
K1 Gaussian noise
K1 Proceso de imágenes
K1 Image processing
K1 Imágenes
K1 Pictures
K1 Informática
K1 Computer science
AB 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.
PB MDPI
YR 2024
FD 2024-03
LK https://hdl.handle.net/10259/11232
UL https://hdl.handle.net/10259/11232
LA eng
NO This research was funded by Generalitat Valenciana under grant CIAICO/2022-051 IMaLeVICS and Spanish Ministry of Science under grant PID2022-140189OB-C21.
DS Repositorio Institucional de la Universidad de Burgos
RD 27-abr-2026