主要研究内容：
人们通过视觉获取外界70%-80%的信息,因此在各种通信方式中，图像通信占特别重要的地位。在图像生成和通讯的过程中，总会产生随机的脉冲干扰和其他的噪声，从而影响图像的质量和视觉效果。为了滤除图像的干扰噪声，现在采用图像去噪方法，主要包括线性滤波和非线性滤波。非线性滤波包括：高斯滤波，中值滤波，底（高）滤波等。而线性滤波包括：卡尔曼滤波和维纳滤波等。这些方法能在一定程度上保持图像的边缘细节，可以克服图像细节模糊，对滤除脉冲干扰及图像扫描噪声很有效，而且能尽可能的滤除噪声，又能更多的保留图像的细节。正因为他们的这些优点，所以文章主要研究以上提到的几种滤波的算法及实现问题。这在红外遥感图像处理，临床医学的辅助诊断以及卫星遥感、图像传输、利用飞行器、雷达、声纳进行军事图像中，有重大应用价值。
Main research contents:
People through the visual access to the outside world 70% -80% of the information, so in a variety of communication methods, image communication accounted for a particularly important place. In the image generation and communication of the process, always produces a random pulse interference and other noise, thus affecting the image quality and visual effects. In order to filter out the interference of the image noise, image denoising method is used, mainly including linear filtering and nonlinear filtering. Nonlinear filtering include: Gaussian filtering, median filtering, at the end of (high) filtering. The linear filtering include: Kalman filtering and Wiener filtering. These methods can to some extent to maintain the edge of the image detail image detail can be overcome blurred right filter pulse noise interference and image scanning is very effective, but also to filter out the noise as much as possible while preserving image more details. It is precisely because they are of these advantages, so the article mentioned above, several major research Filtering algorithm and implementation issues. This infrared remote sensing image processing, clinical medicine for diagnosis and satellite remote sensing, image transmission, using aircraft, radar, sonar, military image, there is a significant application value.
