This paper proposes a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.

中文翻译：

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过滤灰度图像中椒盐噪声的自适应 2 类模糊方法


本文提出了一种新颖的自适应 Type-2 模糊滤波器，用于去除图像中的椒盐噪声。滤波器分两步消除噪声。第一步，根据各自过滤器窗口中的主要隶属函数 (MF) 值将像素分类为好或坏。本文提出了两种通过设计初级 MF 来寻找好像素和坏像素之间阈值的方法。 a) 具有不同均值和相同方差的 MF，b) 具有不同均值和不同方差的 MF。选择 2 类模糊集的主要 MF 作为高斯隶属函数。而在第二步中，对分类为不良的像素进行去噪。对于去噪，本文提出了一种基于好像素加权平均值的新颖的 Type-1 模糊方法。所提出的滤波器针对噪声水平低至 20% 至高至 99% 的多个标准图像进行了验证。结果表明，与其他最先进的算法相比，所提出的滤波器在峰值信噪比值方面表现更好。