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Prof. Laurent Jacques

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Research topics:

Bilinear and Biconvex Inverse Problems for Computational Sensing Systems: A research effort in the solution of blind calibration and deconvolution problems arising in compressive imaging.

Blind Image Deconvolution for Non-parametric PSF estimation in Solar Astronomy: This research proposes to estimate the point spread function (PSF) of an telescope from the observation of the transit of a celestial body (e.g., the Moon or Venus) in front of the Sun. This is achieved by solving a regularized blind deconvolution problem.

Compressive Optical Deflectometric Tomography: The refractive index map allows the optical characterization of complex transparent materials such as optical fibers or intraocular lenses. This research topic addresses the problem of reconstructing the refractive index map of a transparent object from few amount of optical deflectometric measurements. We aim at developing a numerical reconstruction method which makes Optical Deflectometric Tomography compressive and robust to noise.

Compressive Schlieren Deflectometry: This project concerns with the application of compressive sensing principles for characterizing transparent objects using schlieren deflectometry. This is an instance of real world applications of compressed sensing.

Compressed Sensing and High Resolution Quantization: Measurement quantization is a critical step in the design and in the dissemination of new technologies implementing the Compressed Sensing (CS) paradigm. Quantization is indeed mandatory for transmitting, storing and even processing any data sensed by a CS device.

Multiple object tracking with prior detections and graph formalisms: This project considers the tracking of multiple objects within video sequence(s). Fundamentally, it aims at formalising application scenarios in which reliability and the discriminability of the object features vary over time. In order to address problems involving large number of targets, and because automatic detection algorithms have gained maturity, our work assumes that a set of prior and plausible targets detections are available at each time instant.

Bridging 1-bit and High-Resolution Quantized Compressed Sensing with QIHT: In the framework of Quantized Compressed Sensing, we tried to bridge two extreme cases: 1-bit and high resolution quantization. The requirement of consistency of the reconstructed signal with quantized measurement led us to a new reconstruction algorithm called Quantized IHT (QIHT) that outperforms classical algorithms (IHT and BPDN) at low resolutions.

Current projects:

  • AlterSense - Computational Sensing Strategies for Low-Complexity Signal Models. Principal investigator: Prof. Laurent Jacques

Former projects:

  • DETROIT - Déflectrométrie tridimentionnelle résolue en temps, SPW, responsable: Laurent Jacques
  • TSARINE/NANOTIC - Traitement des Signaux Aléatoires pour la Recherche d’Informations Nanoscopiques en Essaims , SPW, responsable: Benoît Macq

Previous seminars:

Publication list on Dial




Last updated September 19, 2017, at 11:23 AM