Edge-preserving wavelet thresholding for image denoising
Journal of Computational and Applied Mathematics
A variable window approach for image denoising
ESPOCO'05 Proceedings of the 4th WSEAS International Conference on Electronic, Signal Processing and Control
Foundations and Trends in Signal Processing
Removal of correlated noise by modeling the signal of interest in the wavelet domain
IEEE Transactions on Image Processing
Complex Gaussian scale mixtures of complex wavelet coefficients
IEEE Transactions on Signal Processing
Image denoising with anisotropic bivariate shrinkage
Signal Processing
Multiscale texture segmentation via a contourlet contextual hidden Markov model
Digital Signal Processing
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Multiresolution signal and image models such as the hidden Markov tree aim to capture the statistical structure of smooth and singular (edgy) regions. Unfortunately, models based on the orthogonal wavelet transform suffer from shift-variance, making them less accurate and realistic. We extend the HMT modeling framework to the complex wavelet transform, which features near shift-invariance and improved angular resolution compared to the standard wavelet transform. The model is computationally efficient (with linear-time computation and processing algorithms) and applicable to general Bayesian inference problems as a prior density for the data. In a simple estimation experiment, the complex wavelet HMT model outperforms a number of high-performance denoising algorithms, including redundant wavelet thresholding (cycle spinning) and the redundant HMT.