Image Denoising Using Bandelets and Hidden Markov Tree Models

In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and strong interscale, interlocation and interdirection dependencies among OBT coefficients are found Then a Hidden Markov tree (HMT) model in OBT domain which can effectively capture all dependencies across scales, locations and directions is developed The main contribution of this paper is that it exploits the edge direction information of OBT coefficients, and proposes an image denoising algorithm (B-HMT) based on HMT model in OBT domain We apply B-HMT to denoise natural images which contaminated by additive Gaussian white noise, and experimental results show that B-HMT outperforms the Wavelet HMT (W-HMT) and Contourlet HMT (C-HMT) in terms of visual effect and objective evaluation criteria