针对红外图像噪声来源复杂且干扰严重，传统的小波阈值方法噪声方差估计偏差较大的问题，提出了一种基于二维经验模态分解(BEMD)子带阈值估计的红外图像去噪方法。通过将噪声图像进行BEMD分解得到二维的内蕴模函数(BIMF)子带，利用高斯混合模型计算各子带噪声方差。由于噪声估计仅考虑噪声系数，减少了特征分量的影响，获得的阈值更准确,再通过自适应算法分别设定各子带阈值将噪声滤除。实验结果表明,该方法避免了硬阈值函数不连续和软阈值函数偏差较大的缺点，图像整体比较清晰,改善了视觉效果。与传统去噪方法相比,其均方误差(MSE)低，峰值信噪比(PSNR)提高了0.5 dB～3 dB,主客观评价均优于其他去噪方法，且当噪声方差加大后优势更加明显。

Due to the complex sources and serious interferences of the infrared image noise, and the traditional wavelet threshold methods for estimation have large deviations. An infrared image denoising method based on threshold estimation of bidimensional empirical mode decomposition (BEMD) sub-band is proposed. The noisy image is decomposed by BEMD into bidimensional intrinsic mode function (BIMF) subbands, Gaussian mixture model is used to calculate noise variance of each sub-band. As the noise estimation only considers the noise components, effects of the feature components are reduced, and then the more accurate threshold is obtained. The adaptive threshold is set to filter the noise. Experimental results show that the proposed method avoids the disadvantage of the hard threshold function and the soft threshold function, the image is relatively clear and visual effects are improved. Compared with traditional denoising methods, its mean square error (MSE) is less than the other methods, and the peak signal to noise ratio (PSNR) increases by 0.5 dB～3 dB. The new method has a better denoising effect, and the more noise variance the more advantages can be obtained.