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Aurélien Bourquard

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Related publications (22)

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Compressed imaging by sparse random convolution

Theo Lasser, Aurélien Bourquard, Antonio Lopez

The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interestin ...

Optical Society of America2016

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and sto ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Compressed Optical Imaging

Aurélien Bourquard

We address the resolution of inverse problems where visual data must be recovered from incomplete information optically acquired in the spatial domain. The optical acquisition models that are involved share a common mathematical structure consisting of a l ...

EPFL2013

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In this work, we propose a continuous-domain stochastic model that can be applied to image data. This model is autoregressive, and accounts for Gaussian-type as well as for non-Gaussian-type innovations. In order to estimate the corresponding parameters fr ...

IEEE2013

Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Binary Compressed Imaging

Michaël Unser, Aurélien Bourquard

Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Autocalibrated Signal Reconstruction From Linear Measurements Using Adaptive Gamp

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

In this paper, we reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the message-passing algorithm called adapt ...

Ieee2013

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In this paper, we propose a new technique for high-quality reconstruction from single digital holographic acquisitions. The unknown complex object field is found as the solution of a nonlinear inverse problem that consists in the minimization of an energy ...

OSA2013

Autocalibrated Signal Reconstruction from Linear Measurements Using Adaptive GAMP

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

IEEE2013

Image Segmentation With Background Correction Using A Multiplicative Smoothing-Spline Model

Michaël Unser, Aurélien Bourquard, Ramtin Madani

This paper presents an image-segmentation method which compensates multiplicative distortions based on smooth regularity assumptions. In this work, we generalize the original Chan-Vese functional to handle a continuous multiplicative bias. In the derivatio ...

Ieee2012