锅炉煤燃烧火焰图像中存在的噪声会对后续的特征提取和温度重建造成不利的影响．为更有效地去除锅炉煤燃烧火焰图像中的噪声，文中提出了一种基于双树复小波域隐马尔可夫树( HMT) 模型的锅炉煤燃烧火焰图像去噪方法．首先对含噪火焰图像进行双树复小波变换，然后依据 HMT 模型分别对双树复小波系数的实部和虚部进行建模，并采用期望值最大算法来估计模型参数，接着利用贝叶斯最小均方误差准则来估计无噪双树复小波系数，最后通过双树复小波逆变换得到去除噪声的火焰图像．实验结果表明，与小波域 VisuShrink 阈值法.

In order to effectively eliminate the noises existing in boiler coal combustion flame images that are unfa-vorable for the subsequent image feature extraction and temperature reconstruction,an image denoising methodbased on the HMT (Hidden Markov Tree) model in dual- tree complex wavelet domain is proposed.In this method,first,a dual- tree complex wavelet transform is performed for noisy flame image.Next,the real part and the imagi-nary part of the dual- tree complex wavelet coefficients are respectively modeled according to the HMT model.Then,the model parameters are estimated by using the expectation maximization algorithm,and the noiseless dual- treecomplex wavelet coefficients are estimated according to the Bayes minimum mean square error (MMSE) criterion.Finally,an inverse dual- tree complex wavelet transform is conducted to obtain denoised flame images.Experimentalresults show that the proposed method is superior to the wavelet VisuShrink threshold method and the method basedon HMT model in wavelet or Contourlet domain because it helps to reduce the noise more effectively and achievehigher peak signal- to- noise ratio.