Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
MMSE Filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domains
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
A filter bank for the directional decomposition of images: theoryand design
IEEE Transactions on Signal Processing
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Multiscale MAP filtering of SAR images
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
Translation-Invariant Contourlet Transform and Its Application to Image Denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast reduction of speckle noise in real ultrasound images
Signal Processing
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In this paper, two algorithms for multiplicative noise reduction, using the undecimated separable wavelet transform, are extended to the nonsubsampled contourlet (NSCT) domain. Such algorithms use either a maximum a posteriori (MAP) or a linear minimum mean square error (LMMSE) filtering approach, and involve the estimation of the moments of the NSCT coefficients up to the fourth order. The MAP filter relies on the conjecture that the NSCT coefficients follow a generalized Gaussian distribution (GGD), whose parameters locally vary. The extension of the denoising algorithms to the NSCT domain is not trivial, because several issues, related to the nonseparable implementation of the NSCT and the estimation of the moments, are to be considered to obtain viable solutions. Simulation results show that the MAP filter always outperforms the LMMSE one, confirming that the nonstationary GGD model is suitable for describing NSCT coefficients. Both denoising algorithms benefit from the multidirectional domain. However, the improvements on the LMMSE filter are greater than those on the MAP filter, showing that denoising in the NSCT domain is less effective when the nonstationary MAP estimator is used.