2026-05-04T08:44:13Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112322026-01-17T01:05:28Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

00925njm 22002777a 4500 dc Almutairi, Khleef author Morillas Gómez, Samuel author Latorre Carmona, Pedro author 2024-03 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. https://hdl.handle.net/10259/11232 10.3390/electronics13061150 2079-9292 Colour image processing Fuzzy inference system Eigenvector analysis Gaussian noise Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method