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Exploiting low dimensional models in sensing, computation and signal processing

Total Cost €


EC-Contrib. €






Project "C-SENSE" data sheet

The following table provides information about the project.


Organization address
postcode: EH8 9YL

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙212˙048 €
 EC max contribution 2˙212˙048 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF EDINBURGH UK (EDINBURGH) coordinator 2˙212˙048.00


 Project objective

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.


year authors and title journal last update
List of publications.
2017 Jonathan H. Mason, Alessandro Perelli, William H. Nailon, Mike E. Davies
Quantitative electron density CT imaging for radiotherapyplanning
published pages: 297-308, ISSN: , DOI: 10.1007/978-3-319-60964-5_26
Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings 2020-01-28
2017 Jonathan H Mason, Alessandro Perelli, William H Nailon, Mike E Davies
Polyquant CT: direct electron and mass density reconstruction from a single polyenergetic source
published pages: , ISSN: 0031-9155, DOI: 10.1088/1361-6560/aa9162
Physics in Medicine and Biology 2020-01-28
2017 Gilles Puy, Mike E. Davies, Remi Gribonval
Recipes for Stable Linear Embeddings From Hilbert Spaces to $ {mathbb {R}}^{m}$
published pages: 2171-2187, ISSN: 0018-9448, DOI: 10.1109/TIT.2017.2664858
IEEE Transactions on Information Theory 63/4 2020-01-28
2018 Gabor Hannak, Alessandro Perelli, Norbert Goertz, Gerald Matz, Mike E. Davies
Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing
published pages: 857-870, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2018.2850754
IEEE Journal of Selected Topics in Signal Processing 12/5 2020-01-28
2017 Jonathan H. Mason, Alessandro Perelli, William H. Nailon, Mike E. Davies
Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?
published pages: 629-640, ISSN: , DOI: 10.1007/978-3-319-60964-5_55
Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings 2020-01-28
2017 Junqi Tang, Mohammad Golbabaee, Mike E. Davies
Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares
published pages: 3377-3386, ISSN: , DOI:
roceedings of the 34th International Conference on Machine Learning, 2017. PMLR 70:3377-3386, 2020-01-28
2018 Jonathan H Mason, Alessandro Perelli, William H Nailon, Mike E Davies
Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion
published pages: 225001, ISSN: 1361-6560, DOI: 10.1088/1361-6560/aae794
Physics in Medicine & Biology 63/22 2020-01-28
2018 Tang, Junqi; Golbabaee, Mohammad; Bach, Francis; Davies, Mike
Rest-Katyusha: Exploiting the Solution\'s Structure via Scheduled Restart Schemes
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 31 31 2020-01-28
2018 Chen, Dongdong; Lv, Jiancheng; Davies, Mike E.
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine
published pages: , ISSN: , DOI:
2 2020-01-28
2018 Mohammad Golbabaee, Mike E. Davies
Inexact Gradient Projection and Fast Data Driven Compressed Sensing
published pages: 6707-6721, ISSN: 0018-9448, DOI: 10.1109/tit.2018.2841379
IEEE Transactions on Information Theory 64/10 2020-01-28

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