An efficient filtering algorithm is required to remove noise and simultaneously protect fine details and important features in the medical images. In this paper, a noise adaptive information set based switching median (NAISM) filter is proposed for the removal of impulse noise. NAISM filter is inspired from fuzzy switching median filter and works on the concept of information sets. Information sets are derived from fuzzy sets to deal with the uncertainty. It works in two phases; first phase identifies noisy pixels and second applies filtering based on an adaptive switching criterion. It is by virtue of this switching criterion and the local effective information surrounding the noisy pixel, the best calculated value replaces the noisy pixel in the selected window. The proposed information set based filter is capable of removing both low and high noise densities and can preserve image details better than the fuzzy filter. The applicability of the proposed filter is demonstrated on different datasets including Berkeley Segmentation Dataset (BSD), medical and real images. The qualitative and quantitative results demonstrate the effectiveness of the proposed approach in suppressing noise over the existing approaches.

需要一种有效的滤波算法来消除噪声并同时保护医学图像中的精细细节和重要特征。本文提出了一种基于噪声自适应信息集的开关中值（NAISM）滤波器，用于去除脉冲噪声。NAISM过滤器的灵感来自模糊切换中值过滤器，并致力于信息集的概念。信息集是从模糊集导出来处理不确定性的。它分两个阶段工作。第一阶段识别噪声像素，第二阶段基于自适应切换标准应用滤波。借助于该切换标准和围绕噪声像素的局部有效信息，最佳计算值替代了所选窗口中的噪声像素。所提出的基于信息集的滤波器能够去除低噪声密度和高噪声密度，并且比模糊滤波器能够更好地保留图像细节。所提出的过滤器的适用性在包括伯克利分段数据集（BSD），医学图像和真实图像在内的不同数据集上得到了证明。定性和定量结果表明，与现有方法相比，该方法在抑制噪声方面是有效的。