Accès à distance ? S'identifier sur le proxy UCLouvain | Saint-Louis
Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models
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Document type | Article de périodique (Journal article) – Article de recherche |
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Access type | Accès restreint |
Publication date | 2018 |
Language | Anglais |
Journal information | "Information and Inference" - Vol. 8, no. 2, p. 205–271 (2018) |
Peer reviewed | yes |
Publisher | Oxford University Press (Oxford) |
issn | 2049-8764 |
Publication status | Publié |
Affiliations |
UCL
- SSH/IACS - Institute of Analysis of Change in Contemporary and Historical Societies UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique |
Keywords | blind-calibration ; non-convex optimization ; compressive sensing ; random projections ; Wirtinger flow |
Links |
- Ahmed Ali, Cosse Augustin, Demanet Laurent, A convex approach to blind deconvolution with diverse inputs, 10.1109/camsap.2015.7383722
- Ahmed (2016)
- Ahmed Ali, Recht Benjamin, Romberg Justin, Blind Deconvolution Using Convex Programming, 10.1109/tit.2013.2294644
- Bahmani Sohail, Romberg Justin, Lifting for Blind Deconvolution in Random Mask Imaging: Identifiability and Convex Relaxation, 10.1137/141002165
- Balzano Laura, Nowak Robert, Blind Calibration of Networks of Sensors: Theory and Algorithms, Networked Sensing Information and Control (2008) ISBN:9780387688435 p.9-37, 10.1007/978-0-387-68845-9_1
- Baraniuk Richard, Davenport Mark, DeVore Ronald, Wakin Michael, A Simple Proof of the Restricted Isometry Property for Random Matrices, 10.1007/s00365-007-9003-x
- Bilen Cagdas, Puy Gilles, Gribonval Remi, Daudet Laurent, Convex Optimization Approaches for Blind Sensor Calibration Using Sparsity, 10.1109/tsp.2014.2342651
- Bjorklund Tomas, Magli Enrico, A parallel compressive imaging architecture for one-shot acquisition, 10.1109/pcs.2013.6737684
- Cambareri Valerio, Jacques Laurent, A non-convex blind calibration method for randomised sensing strategies, 10.1109/cosera.2016.7745690
- Cambareri, 2017 IEEE International Symposium on Information Theory (ISIT 2017) (2017)
- Candès Emmanuel J., Eldar Yonina C., Strohmer Thomas, Voroninski Vladislav, Phase Retrieval via Matrix Completion, 10.1137/151005099
- Candes Emmanuel J., Li Xiaodong, Soltanolkotabi Mahdi, Phase Retrieval via Wirtinger Flow: Theory and Algorithms, 10.1109/tit.2015.2399924
- Candès Emmanuel J., Strohmer Thomas, Voroninski Vladislav, PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming, 10.1002/cpa.21432
- Choudhary Sunav, Mitra Urbashi, On identifiability in bilinear inverse problems, 10.1109/icassp.2013.6638476
- Davenport M.A., Boufounos P.T., Wakin M.B., Baraniuk R.G., Signal Processing With Compressive Measurements, 10.1109/jstsp.2009.2039178
- Degraux, International Traveling Workshop on Interactions between Sparse models and Technology (iTWIST) (2014)
- Donoho D.L., Compressed sensing, 10.1109/tit.2006.871582
- Dumas John P., Lodhi Muhammad A., Bajwa Waheed U., Pierce Mark C., Computational imaging with a highly parallel image-plane-coded architecture: challenges and solutions, 10.1364/oe.24.006145
- Friedlander B., Strohmer T., Bilinear compressed sensing for array self-calibration, 10.1109/acssc.2014.7094464
- Gross David, Recovering Low-Rank Matrices From Few Coefficients in Any Basis, 10.1109/tit.2011.2104999
- Hayat Majeed M., Torres Sergio N., Armstrong Ernest, Cain Stephen C., Yasuda Brian, Statistical algorithm for nonuniformity correction in focal-plane arrays, 10.1364/ao.38.000772
- Herman M.A., Strohmer T., General Deviants: An Analysis of Perturbations in Compressed Sensing, 10.1109/jstsp.2009.2039170
- Kech (2016)
- Li (2016)
- Ling (2015)
- Ling, Inverse Problems (2015)
- Lipor John, Balzano Laura, Robust blind calibration via total least squares, 10.1109/icassp.2014.6854402
- Paige Christopher C., Saunders Michael A., LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares, 10.1145/355984.355989
- Pisier,, The Volume of Convex Bodies and Banach Space Geometry, 94 (1999)
- Puy Gilles, Vandergheynst Pierre, Gribonval Rémi, Wiaux Yves, Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques, 10.1186/1687-6180-2012-6
- Romberg Justin, Compressive Sensing by Random Convolution, 10.1137/08072975x
- Sanghavi, Results Math., 71, 569 (2016)
- Schneider,, Convex Bodies: The Brunn–Minkowski Theory (2013)
- Schülke Christophe, Caltagirone Francesco, Zdeborová Lenka, Blind sensor calibration using approximate message passing, 10.1088/1742-5468/2015/11/p11013
- Sun (2016)
- Tropp Joel A., An Introduction to Matrix Concentration Inequalities, 10.1561/2200000048
- Vershynin Roman, Introduction to the non-asymptotic analysis of random matrices, Compressed Sensing ISBN:9780511794308 p.210-268, 10.1017/cbo9780511794308.006
- Vershynin Roman, How Close is the Sample Covariance Matrix to the Actual Covariance Matrix?, 10.1007/s10959-010-0338-z
Bibliographic reference | Cambareri, Valerio ; Jacques, Laurent. Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models. In: Information and Inference, Vol. 8, no. 2, p. 205–271 (2018) |
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Permanent URL | http://hdl.handle.net/2078.1/194285 |