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PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

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MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

MATHICSE2015

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci, Lester Melie Garcia

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distr ...

Public Library of Science2015

Reconstruction and stability in acousto-optic imaging for absorption maps with bounded variation

The aim of this paper is to propose for the first time a reconstruction scheme and a stability result for recovering from acousto-optic data absorption distributions with bounded variation. The paper extends earlier results in [3] and [5] on smooth absorpt ...

Academic Press Inc Elsevier Science2014

A Probabilistic Framework for Multiple Speaker Localization

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the ti ...

2013

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Rafael Eduardo Carrillo Rangel

Commonly employed reconstruction algorithms in compressed sensing (CS) use the L-2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vec ...

Institute of Electrical and Electronics Engineers2013

A Probabilistic Framework for Multiple Speaker Localization

Idiap2012

Bits from Photons

Feng Yang

The trends in the design of image sensors are to build sensors with low noise, high sensitivity, high dynamic range, and small pixel size. How can we benefit from pixels with small size and high sensitivity? In this dissertation, we study a new image senso ...

EPFL2012

Extreme rainfall in West Africa: A regional modeling

Juliette Ferial Blanchet, Adnan Ali

In a world of increasing exposure of populations to natural hazards, the mapping of extreme rainfall remains a key subject of study. Such maps are required for both flood risk management and civil engineering structure design, the challenge being to take i ...

Amer Geophysical Union2012

Cooperative Sensing via Sequential Detection

Ali H. Sayed

Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each ...

IEEE2010

Distributed bearing estimation via matrix completion

Volkan Cevher

We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance distortionless response (MVDR), multiple signal classification, and maximum likeli ...

2010