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

  1. Ahmed Ali, Cosse Augustin, Demanet Laurent, A convex approach to blind deconvolution with diverse inputs, 10.1109/camsap.2015.7383722
  2. Ahmed (2016)
  3. Ahmed Ali, Recht Benjamin, Romberg Justin, Blind Deconvolution Using Convex Programming, 10.1109/tit.2013.2294644
  4. Bahmani Sohail, Romberg Justin, Lifting for Blind Deconvolution in Random Mask Imaging: Identifiability and Convex Relaxation, 10.1137/141002165
  5. 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
  6. 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
  7. Bilen Cagdas, Puy Gilles, Gribonval Remi, Daudet Laurent, Convex Optimization Approaches for Blind Sensor Calibration Using Sparsity, 10.1109/tsp.2014.2342651
  8. Bjorklund Tomas, Magli Enrico, A parallel compressive imaging architecture for one-shot acquisition, 10.1109/pcs.2013.6737684
  9. Cambareri Valerio, Jacques Laurent, A non-convex blind calibration method for randomised sensing strategies, 10.1109/cosera.2016.7745690
  10. Cambareri, 2017 IEEE International Symposium on Information Theory (ISIT 2017) (2017)
  11. Candès Emmanuel J., Eldar Yonina C., Strohmer Thomas, Voroninski Vladislav, Phase Retrieval via Matrix Completion, 10.1137/151005099
  12. Candes Emmanuel J., Li Xiaodong, Soltanolkotabi Mahdi, Phase Retrieval via Wirtinger Flow: Theory and Algorithms, 10.1109/tit.2015.2399924
  13. Candès Emmanuel J., Strohmer Thomas, Voroninski Vladislav, PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming, 10.1002/cpa.21432
  14. Choudhary Sunav, Mitra Urbashi, On identifiability in bilinear inverse problems, 10.1109/icassp.2013.6638476
  15. Davenport M.A., Boufounos P.T., Wakin M.B., Baraniuk R.G., Signal Processing With Compressive Measurements, 10.1109/jstsp.2009.2039178
  16. Degraux, International Traveling Workshop on Interactions between Sparse models and Technology (iTWIST) (2014)
  17. Donoho D.L., Compressed sensing, 10.1109/tit.2006.871582
  18. 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
  19. Friedlander B., Strohmer T., Bilinear compressed sensing for array self-calibration, 10.1109/acssc.2014.7094464
  20. Gross David, Recovering Low-Rank Matrices From Few Coefficients in Any Basis, 10.1109/tit.2011.2104999
  21. 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
  22. Herman M.A., Strohmer T., General Deviants: An Analysis of Perturbations in Compressed Sensing, 10.1109/jstsp.2009.2039170
  23. Kech (2016)
  24. Li (2016)
  25. Ling (2015)
  26. Ling, Inverse Problems (2015)
  27. Lipor John, Balzano Laura, Robust blind calibration via total least squares, 10.1109/icassp.2014.6854402
  28. Paige Christopher C., Saunders Michael A., LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares, 10.1145/355984.355989
  29. Pisier,, The Volume of Convex Bodies and Banach Space Geometry, 94 (1999)
  30. 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
  31. Romberg Justin, Compressive Sensing by Random Convolution, 10.1137/08072975x
  32. Sanghavi, Results Math., 71, 569 (2016)
  33. Schneider,, Convex Bodies: The Brunn–Minkowski Theory (2013)
  34. Schülke Christophe, Caltagirone Francesco, Zdeborová Lenka, Blind sensor calibration using approximate message passing, 10.1088/1742-5468/2015/11/p11013
  35. Sun (2016)
  36. Tropp Joel A., An Introduction to Matrix Concentration Inequalities, 10.1561/2200000048
  37. Vershynin Roman, Introduction to the non-asymptotic analysis of random matrices, Compressed Sensing ISBN:9780511794308 p.210-268, 10.1017/cbo9780511794308.006
  38. 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)
Permanent URL http://hdl.handle.net/2078.1/194285