目的 滤除发动机表面缺陷图像上的噪声，使发动机表面缺陷信息得以更好地呈现。方法 首先利用小波变换将发动机表面缺陷含噪图像进行系数分解，获取不同的小波系数；接着利用支持向量机对小波分解系数进行分类，以达到将噪声信号与非噪声信号进行分离的效果；最后利用插值运算对硬阀值函数进行优化，以克服函数不连续性引起的振铃效应等弊端，使得去噪后图像能够保持更多的细节信息。通过实验仿真将所提方法以及中值滤波、双边滤波方法的去噪效果进行对比。结果 所提方法去噪后图像与中值滤波以及双边滤波方法去噪后图像相比，具有更高的PSNR值以及SSIM值。测试图像噪声强度为25%时，所提方法去噪后图像的PSNR值以及SSIM值较中值滤波方法去噪分别提高了20.66%以及11.89%，较双边滤波方法去噪分别提高了10.30%以及5.48%。结论 所提方法比中值滤波、双边滤波方法具有更好的去噪效果，能够对发动机表面缺陷图像的噪声进行去除，并较好地保留图像的细节信息。

The work aims to filter the noise from the engine surface defect image so that the engine surface defect information can be better presented. Firstly, wavelet transform was used to decompose the noise image of engine surface defect to obtain different wavelet coefficients. Then, SVM was used to classify the wavelet decomposition coefficients in order to separate the noise signal from the non-noise signal. Finally, interpolation was used to optimize the hard threshold function to overcome the ringing effect caused by the discontinuity of the function, so that the denoised image could keep more details. The denoising effect of the proposed method was compared with that of the median filter and the bilateral filter through experimental simulation. The image denoised by the proposed method had higher PSNR and SSIM values than that denoised by median filtering and bilateral filtering methods. When the noise of tested image was 25%, the PSNR value and SSIM value of the image denoised by the proposed method increased by 20.66% and 11.89% respectively, compared with that denoised by the median filtering method and increased by 10.30% and 5.48% respectively, compared with that denoised by bilateral filter. Therefore, the proposed method has better denoising effect than the median filter and bilateral filter. It can remove the noise of the engine surface defect image and retain the details of the image better.