Abstract: A novel intra-scale and inter-scale correlation image denoising method based on Dual-tree Complex Wavelet Transform（DCWT） domain is presented to achieve the tradeoff between details retainment and noise removal.A neighborhood coefficient differential window is used to compute intra-scale correlations of complex wavelet coefficients in high frequency detail subimage，and intra-scale correlational state is identified according to the smallest error rate Bayesian decision-making rules.A HMT is fitted to the DCWT to describe the correlations between the coefficients across decomposition scales and mark inter-scale correlational state of complex wavelet coefficinents.The product result of intra-scale correlational state and inter-scale correlational state is looked as a new hidden state transition probability for HMT in DCWT.A set of iterative equations is developed using the Expectation-Maximization（EM） algorithm to estimate the model parameters and produce denoising images.Experimental results show that the proposed denoising algorithm is superior to the traditional filtering methods and possible to achieve an excellent balance between suppressing noise effectively and preserving as many image details and edges as possible.