红外图像存在成像模糊、噪声较大等缺点。为了获得良好的检测、识别效果，红外图像的去噪成了很重要的一项工作。简单介绍小波变换的基本原理，并将其分别与中值滤波和主成分分析方法相结合，对缺陷的红外图像进行处理。该去噪方法无需建立在对噪声方差的精确估计上。试验表明，该算法优于传统的滤波去噪法，能同时有效地抑制高斯噪声和椒盐噪声，有利于对缺陷作进一步的分析和判断。

The infrared image has disadvantages, such as the blurred image and the strong noise. In order to get better effects of detection and recognition, image denoising is an important work. The basic principle of wavelet transformation was presented, and it was used to process the infrared image of defects, which was connected with the median filtering and the principle component analysis. The new method did not rely on accurate estimation of noise variance. The experimental results showed that the processing effects were better than traditional methods, and it could effectively reduce the Gaussian and impulse noise at the same time. Presented method could therfore be used for the further analysis and processing of the defects.