Objectif The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques. The ability to identify and exploit good signal representations is pivotal in many signal and data processing tasks. During the last decade sparse representations have provided stunning performance gains for applications such as: imaging coding, computer vision, super-resolution microscopy and most recently in MRI, achieving many-fold acceleration through compressed sensing (CS).However in most real world sensing it is generally not possible to fully adopt the random sampling strategies advocated by CS. Systems are often nonlinear, measurements have limited dynamic range, noise is rarely Gaussian and reconstruction is not always the final goal. Furthermore, iterative reconstruction techniques are often not adopted in commercial imaging systems as they typically incur at least an order of magnitude more computation than traditional techniques. Thus there is a real need for a new framework for generalized computationally accelerated sensing and processing techniques. The research proposed here will build on the PIs recent work in this area and will develop and analyse a much richer class of hierarchical low dimensional signal models, accommodating everything from physical laws to data-driven models such as deep neural networks. It will provide quantitative guidance for system design and address sensing tasks beyond reconstruction including detection, classification and statistical estimation. It will also exploit low dimensional structure to reduce computational cost as well as estimation accuracy, challenging the notion that exploiting prior information must come at a computational cost.This research will result in a new generation of data-driven, physics-aware and task-orientated sensing systems in application domains such as advanced radar, CT and MR imaging and emerging sensing modalities such as multispectral time-of-flight cameras. Champ scientifique natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopynatural sciencescomputer and information sciencesartificial intelligencecomputer visionnatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencescomputer and information sciencesdata sciencedata processingnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Mots‑clés Compressed sensing low dimensional signal models high dimensional statistics computational complexity Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Thème(s) ERC-ADG-2015 - ERC Advanced Grant Appel à propositions ERC-2015-AdG Voir d’autres projets de cet appel Régime de financement ERC-ADG - Advanced Grant Institution d’accueil THE UNIVERSITY OF EDINBURGH Contribution nette de l'UE € 2 212 048,00 Adresse OLD COLLEGE, SOUTH BRIDGE EH8 9YL Edinburgh Royaume-Uni Voir sur la carte Région Scotland Eastern Scotland Edinburgh Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 212 048,00 Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution nette de l'UE Tout développer Tout réduire THE UNIVERSITY OF EDINBURGH Royaume-Uni Contribution nette de l'UE € 2 212 048,00 Adresse OLD COLLEGE, SOUTH BRIDGE EH8 9YL Edinburgh Voir sur la carte Région Scotland Eastern Scotland Edinburgh Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 212 048,00
