A new map estimator for wavelet domain image denoising using vector-based hidden Markov model
There are a number of image denoising methods in the wavelet domain using statistical models. It is known that the performance of such methods can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It is shown that the vector-based hidden Markov model (VB-HMM) is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross orientation dependencies of the wavelet coefficients. In view of this, we propose a new maximum a posteriori estimator using the VB-HMM as a prior for the wavelet coefficients of images. This is realized by deriving an efficient closed-form expression for the shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the state-of-the-art techniques in terms of both the peak signal to noise ratio and perceptual quality.