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Benefits Of Consistency In Image Denoising With Steerable Wavelets

Publications associées (33)

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Which Wavelet Bases Are the Best for Image Denoising?

Michaël Unser, Thierry Blu, Florian Luisier

We use a comprehensive set of non-redundant orthogonal wavelet transforms and apply a denoising method called SUREshrink in each individual wavelet subband to denoise images corrupted by additive Gaussian white noise. We show that, for various images and a ...

SPIE2005

Integrated Wavelet Processing and Spatial Statistical Testing of fMRI Data

Michaël Unser, Dimitri Nestor Alice Van De Ville, Thierry Blu

We introduce an integrated framework for detecting brain activity from fMRI data, which is based on a spatial discrete wavelet transform. Unlike the standard wavelet-based approach for fMRI analysis, we apply the suitable statistical test procedure in the ...

Elsevier2004

Intra-Adaptive Motion-Compensated Lifted Wavelets for Video Coding

Pierre Vandergheynst, Oscar Divorra Escoda

In this work, we study the effect of inserting spatially local temporal adaptivity to motion compensated frame adaptive transforms for video coding. Motion compensation aligns the temporal wavelet decomposition along motion trajectories. However, valid tra ...

2004

Guest Editorial: Wavelets in Medical Imaging

Michaël Unser

I. Introduction Wavelets are the result of collective efforts that recognized common threads between ideas and concepts that had been independently developed and investigated by distinct research communities. They provide a unifying framework for decompos ...

2003

Orthogonal Hilbert transform filter banks and wavelets

Martin Vetterli, Thierry Blu

Complex wavelet transforms offer the opportunity to perform directional and coherent processing based on the local magnitude and phase of signals and images. Although denoising, segmentation, and image enhancement are significantly improved using complex w ...

IEEE2003

The finite ridgelet transform for image representation

Martin Vetterli, Minh Do

The ridgelet transform (Candès and Donoho, 1999) was introduced as a sparse expansion for functions on continuous spaces that are smooth away from discontinuities along lines. In this paper, we propose an or- thonormal version of the ridgelet transform for ...

2003

Fractal additive synthesis

Pietro Polotti

Musical and audio signals in general form a major part of the large amount of data exchange taking place in our information-based society. Transmission of high quality audio signals through narrow-band channels, such as the Internet, requires refined metho ...

EPFL2003

Discrete Ridgelet Transforms for Image Representation

Martin Vetterli, Minh Do

The ridgelet transform (Candes and Donoho, 1999) was introduced as a new multiscale representation for functions on continuous spaces that are smooth away from discontinuities along lines. In this paper, we present several discrete versions of the ridgelet ...

2001

Adaptive wavelet thresholding for image denoising and compression

Martin Vetterli

The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresh- olding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distr ...

2000

Spatially adaptive wavelet thresholding with context modeling for image denoising

Martin Vetterli

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not ...

2000