摘 要
图像在获取和传输过程中会受到各种噪声的干扰，从而使得图像退化，造成图像质量下降。图像退化会引起图像模糊和特征淹没，从而不利于图像分析。为了去除噪声并改善图像质量，需要对图像进行去噪处理，从而有必要研究图像去噪算法。
图像去噪算法有很多种，可以分别在空间域和频率域中进行。论文综述了平均值滤波、中值滤波、空间域低通滤波、多幅图像求平均法和频域低通滤波去噪算法。首先介绍了噪声的概念、产生的原因、分类及其特点。接着介绍了平均值滤波和中值滤波算法的基本原理及其适用范围，最后介绍了空间域低通滤波、频率域低通滤波和多幅图像求平均算法的基本原理。
论文遵循理论联系实践，理论实践并重的研究思路。不仅对各种去噪算法的理论基础和滤波原理作了详细的论述，而且使用MATLAB程序进行仿真并分析了去噪效果。论文最后设计了图形用户界面来评价各种算法的去噪效果。
MATLAB仿真结果表明：各种去噪算法各有其优缺点。因此，在对一幅图像去噪之前，首先要分析噪声类型及其产生原因，然后再选择恰当的去噪算法，这样才能得到比较令人满意的去噪效果。
关键词：图像噪声；图像去噪算法；MATLAB；图形用户界面
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Abstract
The image can be affected by noise during the process of acquisition and transmission. The image noise leads to image degeneration and drop in quality. Image degeneration can cause image blur as well as characteristic masking so that unfit for image analysis. In order to remove noise and improve image quality, noise reduction is needed. Therefore, it is necessary to study image noise reduction algorithms.
There are many kinds of image noise reduction algorithms and they can be implemented in spatial domain and frequency domain respectively. This paper summarizes such algorithm as mean filter, median filter, low pass filter in spatial domain, mean of multi-image adding and low pass filter in frequency domain. Firstly, conception, causing, classification and characteristic of noise are introduced. Secondly, the basic principle and application range of mean filter and median filter algorithm is introduced. Finally, the basic principle of low pass filter in spatial domain, low pass filter in frequency domain and mean of multi-image adding algorithm is introduced.
This paper pays both attention to theory and practice. It not only summarizes the theory and filter principle of different image noise reduction algorithms in detail, but also simulates them using MATLAB procedure and analyzes their noise reduction results. The graphic user interface is designed to evaluate the result of noise reduction to different image noise reduction algorithms The MATLAB simulation results demonstrate that different noise reduction algorithms are of different advantages and disadvantages. Therefore, type and causing of noise should be analyzed first and then appropriate noise reduction algorithm is selected before image noise reduction so as to acquire satisfactory results.
Key words: image noise； image noise reduction algorithm；MTALAB； graphic user interface
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