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Abstract: Images from positron emission tomography (PET) provide metabolic information about the human body. They present, however, a spatial resolution that is limited by physical and instrumental factors often modeled by a blurring function.

Abstract: The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details.

Abstract: This paper studies the fast acquisition of Hyper- Spectral (HS) data using Fourier transform interferometry (FTI). FTI has emerged as a promising alternative to capture, at a very high resolution, the wavelength coordinate as well as the spatial domain of the HS volume.

Abstract: Positron emission tomography is more and more used in radiation oncology, since it conveys useful functional information about cancerous lesions. Its rather low spatial resolution, however, prevents accurate tumor delineation and heterogeneity assessment.

Abstract: Given a set of data points lying on a smooth manifold, we present methods to interpolate those with piecewise Bézier splines. The spline is composed of Bézier curves (resp. surfaces) patched together such that the spline is continuous and differentiable at any point of its domain.

Abstract: Direct exoplanets imaging is a challenging task for two main reasons. First, the host star is several order of magnitude brighter than exoplanets. Second, the great distance between us and the star system makes the exoplanets-star angular dis- tance very small.

Abstract: Real-world applications of compressed sensing are often limited by modelling errors between the sensing operator, which is necessary during signal recovery, and its actual physical implementation. In this paper we tackle the biconvex problem of recovering a sparse input signal jointly with some unknown and unstructured multiplicative factors affecting the sensors that capture each measurement.

Abstract: This paper investigates non-uniform guarantees of \(ell_1\) minimization, subject to an \(ell_infty\) data fidelity constraint, to stably recover the support of a sparse vector when solving noisy linear inverse problems.

Abstract: In this paper, we study the support recovery guarantees of underdetermined sparse regression using the ℓ1-norm as a regularizer and a non-smooth loss function for data fidelity. More precisely, we focus in detail on the cases of ℓ1 and ℓ∞ losses, and contrast them with the usual ℓ2 loss.