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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.

Abstract: A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters. Such an agile imaging technique comes at the cost of a reduced spatial resolution and the need for a demosaicing procedure on its interleaved data.

Abstract: Randomized sets of binary tests have appeared to be quite effective in solving a variety of image processing and vision problems. The exponential growth of their memory usage with the size of the sets however hampers their implementation on the memory-constrained hardware generally available on low-power embedded systems.

Abstract: Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible.

Abstract: The 4D RecOnstructiOn using Spatial and TEmpo- ral Regularization method is a recent 4D cone beam computed tomography algorithm. 4D ROOSTER has not been rigorously proved to converge. This paper aims to reformulate it using the Chambolle & Pock primal-dual optimization scheme.

Abstract: It is well-known that the performance of the Gaussian mixture model (GMM) based acoustic-to-articulatory inversion (AAI) improves by either incorporating smoothness constraint directly in the inversion criterion or smoothing (low-pass filtering) estimated articulator tra- jectories in a post-processing step, where smoothing is performed independently of the inversion.