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Blind optimization of algorithm parameters for signal denoising by Monte-Carlo sure

Related publications (57)

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A Projected Gradient Algorithm for Image Restoration Under Hessian Matrix-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order extension of the total variation (TV) functional. These regularizers retain some of the most favorable properties of TV while they can effectively deal with t ...

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

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

Unbiased Model Combinations for Adaptive Filtering

Ali H. Sayed

In this paper, we consider model combination methods for adaptive filtering that perform unbiased estimation. In this widely studied framework, two adaptive filters are run in parallel, each producing unbiased estimates of an underlying linear model. The o ...

IEEE2010

Undecimated Haar Thresholding For Poisson Intensity Estimation

Michaël Unser, Thierry Blu, Florian Luisier

We propose a novel algorithm for denoising Poisson-corrupted images, that performs a signal-adaptive thresholding of the undecimated Haar wavelet coefficients. A Poisson's unbiased MSE estimate is devised and adapted to arbitrary transform-domain pointwise ...

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

Undecimated Haar Thresholding for Poisson Intensity Estimation

Michaël Unser, Thierry Blu, Florian Luisier

IEEE2010

Multiuser Two-Way Amplify-and-Forward Relay Processing and Power Control Methods for Beamforming Systems

Ali H. Sayed

In this paper, multiple-input multiple-output (MIMO) relay transceiver processing is proposed for multiuser two-way relay communications. The relay processing is optimized based on both zero-forcing (ZF) and minimum mean-square-error (MMSE) criteria under ...

IEEE2010

Comparative Study of Three Bayesian Filtering Approaches for Phase Step Estimation in Optical Interferometry

Pramod Rastogi, Abhijit Patil

This paper presents three recursive Bayesian filtering approaches in phase shifting interferometry involving a piezoelectric device (PZT). The three approaches are extended Kalman filtering, unscented Kalman filtering, and particle filtering. These approac ...

2010