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The Rare Eclipse Problem on Tiles: Quantised Embeddings of Disjoint Convex Sets

Abstract: Quantised random embeddings are an efficient dimensionality reduction technique which preserves the distances of low-complexity signals up to some controllable additive and multiplicative distortions. In this work, we instead focus on verifying when this technique preserves the separability of two disjoint closed convex sets, i.
2017 Proceedings of the 12th International Conference of Sampling Theory and Applications, Tallinn, 2017
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A Non-Convex Approach to Blind Calibration for Linear Random Sensing Models

Abstract: Performing blind calibration is highly important in modern sensing strategies, particularly when calibration aided by multiple, accurately designed training signals is infeasible or resource-consuming. We here address it as a naturally-formulated non-convex problem for a linear model with sub-Gaussian ran- dom sensing vectors in which both the sensor gains and the sig- nal are unknown.
2016 Proceedings of the third international Traveling Workshop on Interactions between Sparse models and Technology iTWIST'16
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A Non-Convex Approach to Blind Calibration from Linear Sub-Gaussian Random Measurements

Abstract: Blind calibration is a bilinear inverse problem arising in modern sensing strategies, whose solution becomes crucial when traditional calibration aided by multiple, accurately designed training signals is either infeasible or resource-consuming.
2016 Proceedings of the “37th WIC Symposium on Information Theory in the Benelux, 6th joint WIC/IEEE SP Symposium on Information Theory and Signal Processing in the Benelux”
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A non-convex blind calibration method for randomised sensing strategies

Abstract: The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue.
2016 Proceedings of International Workshop on “Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa)”, 2016
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Blind Deconvolution of PET Images using Anatomical Priors

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.
2016 iTWIST'16
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Cell segmentation with random ferns and graph-cuts

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.
2016 Proceedings of IEEE International Conference on Image Processing
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Coded-Illumination Fourier Transform Interferometry

2016 Golden Jubilee Meeting of the Royal Belgian Society for Microscopy (RBSM)
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Compressive Hyperspectral Imaging with Fourier Transform Interferometry

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.
2016 Proceedings of the third international Traveling Workshop on Interactions between Sparse models and Technology (iTWIST'16), arXiv:1609.04167
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Image Deconvolution by Local Order Preservation of Pixels Values

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.
2016 IEEE
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Interpolation on manifolds using Bézier functions

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.
2016 “Proceedings of the third international Traveling Workshop on Interactions between Sparse models and Technology iTWIST'16”
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