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Denoising of images using fuzzy rulebase and clustering approach

机译:使用模糊规则库和聚类方法对图像进行去噪

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Detection of pixels corrupted by noise and assessing the degree to which the pixels are corrupted intrinsically fuzzy processes, involve uncertainty and imprecision. The paper aims at reconstruction of the image with ensured quality after removing noise from the original image. Here region marking process has been introduced to obtain number of clusters automatically which partition the whole image into several regions. Noise pixels in each region are detected by applying fuzzy rules constructed using gradient information of the image. The proposed approach reproduces the image with promising quality compare to the existing image denoising methods face. It also gives a comparative study between the existing filters with the proposed one not only for grey scale images but also the colour images.
机译:检测被噪声破坏的像素并评估像素被破坏的程度本质上是模糊过程,涉及不确定性和不精确性。本文旨在从原始图像中去除噪声后,以确保质量的图像重建。此处引入了区域标记过程,以自动获取将整个图像划分为几个区域的聚类数量。通过应用使用图像的梯度信息构造的模糊规则来检测每个区域中的噪声像素。与现有的图像去噪方法相比,所提出的方法能够以有希望的质量再现图像。还对现有滤镜与提出的滤镜进行了比较研究,该滤镜不仅针对灰度图像,而且针对彩色图像。

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