关键词: 均匀离散曲波变换, 互信息, 隐马尔可夫树模型（HMT）, 最大期望（EM）算法, 图像去噪

Abstract: Based on the statistical properties of coefficients of the Uniform Discrete Curvelet Transform（UDCT）, and the analysis of correlation metric mutual information about the coefficients, this paper chooses the Hidden Markov Tree to model the coefficients finally and trains the sequence with the EM algorithm. With amount of time consuming, an optimization EM algorithm based on HMT of UDCT coefficients is presented; it further optimizes the algorithm by defining the variance and state transition matrix based on the attenuation of coefficients and continuity between the scales. Experimental results show that, in the use of similarity and Peak Signal to Noise Ratio effect as the measurement of image de-noising, under the same conditions, the algorithm proposed has better real-time and de-noising effect than the Wavelet HMT, Contourlet HMT, UDCT HMT algorithm.

Key words: Uniform Discrete Curvelet Transform（UDCT）, mutual information, Hidden Markov Tree model（HMT）, Expectation-Maximization（EM） algorithm, image denoising