中文摘要：

为避免小波去噪时阈值的缺陷和非局部均值滤波去噪时计算的复杂性和更有效地去除红外图像中的噪声，提出了一种采用非局部均值滤波的小波图像去噪方法。对含噪图像进行多层小波分解，采用新的贝叶斯估计阈值对高频系数进行阈值化处理，以消除高频噪声；在部分低层子带上进行非局部均值处理以进一步消除噪声。实验结果表明，与通常的小波阈值去噪和非局部均值去噪相比，该方法能很好地去除红外图像中的噪声，获得更高的信噪比（Signal To Noise Ratio，SNR）和更小的均方误差（Mean Squared Error，MSE），而且该方法计算相对简单，能达到很好的视觉效果。

英文摘要：

To avoid the limitation of wavelet thresholding and the calculation complexity of non-local means filtering when an image is denoised, a more effective wavelet image denoising method based on Non Local Means （NLM） is proposed. Firstly, multi-level wavelet decomposition is carried out for an image containing noises. Then, a new BayesShrink estimation threshold is used to implement thresholding processing of the sub-band coefficients so as to remove the high frequency noise. Finally, to further remove the noise, NLM processing is implemented in part low-level sub-bands. The experimental result shows that compared with the common wavelet threshold denoising and NLM filtering methods, this method can remove the noises in an infrared image more effectively and can obtain a higher Signal-to-Noise Ratio （SNR） and a lower Mean Square Error （MSE）. Moreover, the method is relatively simple in calculation and can achieve excellent visual effect.