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Monte-Carlo SURE: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms

Publications associées (74)

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A Dual Algorithm for $ L _{ 1 } $ -Regularized Reconstruction of Vector Fields

Michaël Unser, Emrah Bostan, Pouya Dehghani Tafti

Recent advances in vector-field imaging have brought to the forefront the need for efficient denoising and reconstruction algorithms that take the physical properties of vector fields into account and can be applied to large volumes of data. With these req ...

IEEE2012

Distributed pareto-optimal solutions via diffusion adaptation

Ali H. Sayed

We consider solving multi-objective optimization problems in a distributed manner over a network of nodes. The problem is equivalent to optimizing a global cost that is the sum of individual components. Diffusion adaptation enables the nodes to cooperate l ...

IEEE2012

Optimal combination rules for adaptation and learning over networks

Ali H. Sayed

Adaptive networks, consisting of a collection of nodes with learning abilities, are well-suited to solve distributed inference problems and to model various types of self-organized behavior observed in nature. One important issue in designing adaptive netw ...

IEEE2011

Image Denoising in Mixed Poisson-Gaussian Noise

Michaël Unser, Thierry Blu, Florian Luisier

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds ...

Institute of Electrical and Electronics Engineers2011

Stochastic Models for Sparse and Piecewise-Smooth Signals

Michaël Unser, Pouya Dehghani Tafti

We introduce an extended family of continuous-domain stochastic models for sparse, piecewise-smooth signals. These are specified as solutions of stochastic differential equations, or, equivalently, in terms of a suitable innovation model; the latter is ana ...

IEEE2011

Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition.

Philip Neil Garner

Cepstral normalisation in automatic speech recognition is investigated in the context of robustness to additive noise. It is argued that such normalisation leads naturally to a speech feature based on signal to noise ratio rather than absolute energy (or p ...

Idiap2011

Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition

Philip Neil Garner

Cepstral normalisation in automatic speech recognition is investigated in the context of robustness to additive noise. In this paper, it is argued that such normalisation leads naturally to a speech feature based on signal to noise ratio rather than absolu ...

2011

Robustness of Group Delay Representations for Noisy Speech Signals

Sree Hari Krishnan Parthasarathi

This paper demonstrates the robustness of group delay based features to additive noise. First, we analytically show the robustness of group delay based represen- tations. The analysis makes use of the fact that, for minimum-phase signals, the group delay f ...

2011

The SURE-LET approach to image denoising

Florian Luisier

Denoising is an essential step prior to any higher-level image-processing tasks such as segmentation or object tracking, because the undesirable corruption by noise is inherent to any physical acquisition device. When the measurements are performed by phot ...

EPFL2010

Strategies for Delay-Limited Source-Channel Coding

Marius Kleiner

In point-to-point source-channel communication with a fidelity criterion and a transmission cost constraint, the region of achievable cost and fidelity pairs is completely characterized by Shannon's separation theorem. However, this is in general only true ...

EPFL2010