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Jointly low-rank and bisparse recovery: Questions and partial answers

Abstract: We investigate the problem of recovering jointly \(r\)-rank and \(s\)-bisparse matrices from as few linear measurements as possible, considering arbitrary measurements as well as rank-one measurements. In both cases, we show that \(m \asymp r s \ln(en/s)\) measurements make the recovery possible in theory, meaning via a nonpractical algorithm.
2019 Analysis and Applications
DOI DIAL arXiv

Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.
2019 Information and Inference
DOI DIAL arXiv

STIM map: detection map for exoplanets imaging beyond asymptotic Gaussian residual speckle noise

Abstract: Direct imaging of exoplanets is a challenging task as it requires to reach a high contrast at very close separation to the star. Today, the main limitation in the high-contrast images is the quasi-static speckles that are created by residual instrumental aberrations.
2019 Monthly Notices of the Royal Astronomical Society
DOI DIAL arXiv

Determination of vibration amplitudes from binary phase patterns obtained by phase-shifting time-averaged speckle shearing interferometry

Abstract: Speckle shearing interferometry (shearography) is a full-field strain measurement technique that can be used in vibration analysis. In our case, we apply a method that combines the time-averaging and phase-shifting techniques.
2018 Applied Optics
DOI DIAL

Multispectral Compressive Imaging Strategies Using Fabry–Pérot Filtered Sensors

Abstract: In this paper, we introduce two novel acquisition schemes for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they use a dedicated sensor that integrates narrowband Fabry–Pérot spectral filters at the pixel level.
2018 IEEE Transactions on Computational Imaging
DOI DIAL arXiv

Quantized Compressive K-Means

Abstract: The recent framework of compressive statistical learning proposes to design tractable learning algorithms that use only a heavily compressed representation - or sketch - of massive datasets. Compressive K-Means (CKM) is such a method: It aims at estimating the centroids of data clusters from pooled, nonlinear, and random signatures of the learning examples.
2018 IEEE Signal Processing Letters
DOI DIAL arXiv

Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models

Abstract: Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by using multiple, accurately chosen training signals to recover the missing information on the sensing model, an approach that can be resource-consuming and cumbersome.
2018 Information and Inference
DOI DIAL arXiv

Discriminative and efficient label propagation on complementary graphs for multi-object tracking

Abstract: Given a set of detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs, each graph capturing how either the spatiotemporal or the appearance cues promote the assignment of identical or distinct labels to a pair of detections.
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI DIAL arXiv

On the Noise Robustness of Simultaneous Orthogonal Matching Pursuit

Abstract: In this paper, the joint support recovery of several sparse signals whose supports present similarities is examined. Each sparse signal is acquired using the same noisy linear measurement process, which returns fewer observations than the dimension of the sparse signals.
2017 IEEE Transactions on Signal Processing
Cite DOI DIAL arXiv

Small Width, Low Distortions: Quantized Random Embeddings of Low-complexity Sets

Abstract: Under which conditions and with which distortions can we preserve the pairwise-distances of low-complexity vectors, e.g., for structured sets such as the set of sparse vectors or the one of low-rank matrices, when these are mapped in a finite set of vectors?
2017 IEEE Transactions on Information Theory
DOI DIAL arXiv