尽管中值滤波以及各种改进方法是去除图像中随机值脉冲噪声的有效方法，然而，大多数去噪方法存在门限值选取困难和对图像边缘纹理结构过平滑的缺点。针对这一问题，提出了一种基于几何结构的用于检测和去除随机值脉冲噪声的新方法。该方法首先利用图像的直方图分布来估计脉冲噪声的噪声率；然后进一步基于噪声率和细节图像的直方图分布，自适应地确定两个分类门限；最后利用两个门限，将细节图像中的像素分成‘未被污染点’、‘待定点’和‘噪声点’。其中‘待定点’主要由边缘和纹理区像素和噪声像素构成，为区分其属性，还引入了几何结构检测方法。基于各像素点的类型，细节图像被用于修正中值滤波的结果。实验结果表明，该新方法在去除脉冲噪声的同时，还很好地保留了图像的边缘结构。与已有的方法相比，具有明显的优势。

The median filtering method and its improved methods is an effective approach to remove random value impulse noise in images. However, most methods have the same shortcomings in finding the optimal threshold and the edges and over smoothed textures structure of images. In the paper, we propose a novel method based on the geometric structure detection to remove random value impulse noise from images. First, the histogram of a noisy image is used to estimate the noise ratio. Next, the two thresholds are adaptively determined from the noise ratio and the histogram of the detail image. Utilizing these two thresholds, all pixels in the detail image are divided into three sets: ‘uncorrupted pixels’, ‘undetermined pixels’ and ‘noise pixels’. The set of ‘undetermined pixels’ is often composed of pixels in edges and textures as well as noise pixels. Finally, the geometric structure detection is proposed to distinguish ‘undetermined pixels’. Based on types of each pixel, the result of the median filtering is modified using the detail image. The simulation results show that the proposed method can remove impulse noise while preserve the edge structure of the image. It is superior to the existing methods in performance.