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The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

Related publications (46)

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Benefits Of Consistency In Image Denoising With Steerable Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Bugra Tekin

The steerable wavelet transform is a redundant image representation with the remarkable property that its basis functions can be adaptively rotated to a desired orientation. This makes the transform well-suited to the design of wavelet-based algorithms app ...

Ieee2013

Benefits of Consistency in Image Denoising with Steerable Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

IEEE2013

Rate Distortion Behavior of Sparse Sources

Martin Vetterli

The rate distortion behavior of sparse memoryless sources is studied. These serve as models of sparse signal representations and facilitate the performance analysis of "sparsifying" transforms like the wavelet transform and nonlinear approximation schemes. ...

Institute of Electrical and Electronics Engineers2012

Two-Dimensional Discrete Wavelet Transform on Large Images for Hybrid Computing Architectures: GPU and CELL

Pawel Lichocki

The Discrete Wavelet Transform (DWT) has gained the momentum in signal processing and image compression over the last decade bringing the concept up to the level of new image coding standard JPEG2000. Thanks to many added values in DWT, in particular inher ...

Springer Berlin Heidelberg2012

Optimally Localized Wavelets and Smoothing Kernels

Kunal Narayan Chaudhury

It is well-known that the Gaussian functions and, more generally, their modulations-translations (the Gabor functions) have the unique property of being optimally localized in space and frequency in the sense of Heisenberg's uncertainty principle. In this ...

EPFL2011

Sparse Image Representation by Directionlets

Martin Vetterli, Vladan Velisavljevic, Pier Luigi Dragotti

Despite the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation are limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertic ...

Elsevier2010

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Accelerated Wavelet-Regularized Deconvolution For 3-D Fluorescence Microcopy

Michaël Unser, Matthieu Guerquin-Kern, Ilker Bayram, Raquel Terrés Cristofani

Modern deconvolution algorithms are often specified as minimization problems involving a non-quadratic regularization functional. When the latter is a wavelet-domain l(1)-norm that favors sparse solutions, the problem can be solved by a simple iterative sh ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Multiresolution Monogenic Signal Analysis Using the Riesz-Laplace Wavelet Transform

Michaël Unser, Dimitri Nestor Alice Van De Ville, Daniel Sage

The monogenic signal is the natural 2-D counterpart of the 1-D analytic signal. We propose to transpose the concept to the wavelet domain by considering a complexified version of the Riesz transform which has the remarkable property of mapping a real-value ...

Institute of Electrical and Electronics Engineers2009

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We propose a reconstruction scheme adapted to MRI that takes advantage of a sparsity constraint in the wavelet domain. We show that artifacts are significantly reduced compared to conventional reconstruction methods. Our approach is also competitive with ...

IEEE2009