Image denoising in wavelet domain using the vector-based hidden Markov model
Denoising problems can be regarded as that of a prior probability modeling in an estimation task. The performance of the estimator is intimately related on the correctness of the model. This paper proposes a new wavelet-domain image denoising method using the minimum mean square error (MMSE) estimator. The vector-based hidden Markov model (HMM) is used as the prior for modeling the wavelet coefficients of an image. This model is an effective statistical model for the wavelet coefficients, since it is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross-orientation dependencies of the wavelet coefficients. Using this prior, a Weiner filter, which is derived using a MMSE estimator, is developed for estimating the denoised coefficients. Experiments are conducted on standard images to evaluate the performance of the proposed method. Simulation results are provided to show that the proposed denoising method can effectively reduce the noise in yielding higher values for the peak signal-to-noise ratio along with better visual quality than that provided by some of the other existing methods.