Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
The design of approximate Hilbert transform pairs of wavelet bases
IEEE Transactions on Signal Processing
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
Image denoising based on hierarchical Markov random field
Pattern Recognition Letters
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We combine the techniques of the complex wavelet transform and Markov random fields (MRF) model to restore natural images in white Gaussian noise. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, directionality and complexity. The prior MRF model is used to exploit the clustering property of the wavelet transform, which can effectively remove annoying pointlike artifacts associated with standard wavelet denoising methods. Our experimental results significantly outperform those using standard wavelet transforms and are comparable to those from overcomplete wavelet transforms and MRFs, but with much less complexity.