| Issue |
J. Space Weather Space Clim.
Volume 6, 2016
Statistical Challenges in Solar Information Processing
|
|
|---|---|---|
| Article Number | A1 | |
| Number of page(s) | 20 | |
| DOI | https://doi.org/10.1051/swsc/2015040 | |
| Published online | 11 January 2016 | |
- Almeida, M., and L. Almeida. Blind and semi-blind deblurring of natural images. IEEE Trans. Image Process., 19 (1), 36–52, 2010, DOI: 10.1109/TIP.2009.2031231. [CrossRef] [Google Scholar]
- Almeida, M., and M. Figueiredo. Deconvolving images with unknown boundaries using the alternating direction method of multipliers. IEEE Trans. Image Process., 22 (8), 3074–3086, 2013a, DOI: 10.1109/TIP.2013.2258354. [CrossRef] [Google Scholar]
- Almeida, M., and M. Figueiredo. Parameter estimation for blind and non-blind deblurring using residual whiteness measures. IEEE Trans. Image Process., 22 (7), 2751–2763, 2013b, DOI: 10.1109/TIP.2013.2257810. [CrossRef] [Google Scholar]
- Anconelli, B., M. Bertero, P. Boccacci, M. Carbillet, and H. Lanteri. Reduction of boundary effects in multiple image deconvolution with an application to LBT LINC-NIRVANA. A&A, 448 (3), 1217–1224, 2006, DOI: 10.1051/0004-6361:20053848. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Anscombe, F.J. The transformation of Poisson, binomial and negative-binomial data. Biometrika, 35 (3–4), 246–254, 1948, DOI: 10.1093/biomet/35.3-4.246. [CrossRef] [Google Scholar]
- Anthoine, S., J.-F. Aujol, Y. Boursier, and C. Mélot. Some proximal methods for Poisson intensity CBCT and PET. Inverse Prob. Imaging, 6 (4), 565–598, 2012, DOI: 10.3934/ipi.2012.6.565. [CrossRef] [Google Scholar]
- Attouch, H., J. Bolte, P. Redont, and A. Soubeyran. Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka-Łojasiewicz inequality. Math. Oper. Res., 35 (2), 438–457, 2010, DOI: 10.1287/moor.1100.0449. [Google Scholar]
- Ayers, G.R., and J.C. Dainty. Iterative blind deconvolution method and its applications. Opt. Lett., 3 (7), 547–549, 1988, DOI: 10.1364/OL.13.000547. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Babacan, S., R. Molina, and A. Katsaggelos. Variational Bayesian blind deconvolution using a total variation prior. IEEE Trans. Image Process., 18 (1), 12–26, 2009, DOI: 10.1109/TIP.2008.2007354. [CrossRef] [Google Scholar]
- Beck, A., and M. Teboulle. Gradient-based algorithms with applications to signal recovery problems. In: D., Palomar, and Y. Eldar, Editors. Convex Optimization in Signal Processing and Communications, Cambridge University Press, 42–88, 2010. [Google Scholar]
- Berg, E.V., M.P. Friedlander, G. Hennenfent, F. Herrmann, R. Saab, and Ö. Yılmaz. Sparco: A testing framework for sparse reconstruction, Tech. Rep. TR-2007-20, Dept. Computer Science, University of British Columbia, Vancouver, 2007. [Google Scholar]
- Carrillo, R.E., J.D. McEwen, and Y. Wiaux. Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging. Mon. Not. R. Astron. Soc., 426 (2), 1223–1234, 2012, DOI: 10.1111/j.1365-2966.2012.21605.x. [Google Scholar]
- Chambolle, A., and T. Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vision, 40 (1), 120–145, 2011, DOI: 10.1007/s10851-010-0251-1. [Google Scholar]
- Chang, S., B. Yu, and M. Vetterli. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process., 9 (9), 1532–1546, 2000, DOI: 10.1109/83.862633. [NASA ADS] [CrossRef] [Google Scholar]
- Combettes, P.L., and J.-C. Pesquet. Proximal splitting methods in signal processing. In: H.H., Bauschke, R.S. Burachik, P.L. Combettes, V. Elser, D.R. Luke, and H. Wolkowicz, Editors. Fixed-point algorithms for inverse problems in science and engineering, vol. 49 of Springer optimization and its applications, Springer, New York, 185–212, ISBN: 978-1-4419-9568-1, 2011, DOI: 10.1007/978-1-4419-9569-8_10. [Google Scholar]
- DeForest, C.E., P.C.H. Martens, and M.J. Wills-Davey. Solar coronal structure and stray light in TRACE. Astrophys. J., 690, 1264–1271, 2009, DOI: 10.1088/0004-637X/690/2/1264. [Google Scholar]
- Dong, W., H. Feng, Z. Xu, and Q. Li. A piecewise local regularized Richardson-Lucy algorithm for remote sensing image deconvolution. Opt. Laser Technol., 43 (5), 926–933, 2011, DOI: 10.1016/j.optlastec.2010.12.012. [CrossRef] [Google Scholar]
- Donoho, D. De-noising by soft-thresholding. IEEE Trans. Inf. Theory, 41 (3), 613–627, 1995, DOI: 10.1109/18.382009. [Google Scholar]
- Donoho, D.L., and I.M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc., 90 (432), 1200–1224, 1995, DOI: 10.1080/01621459.1995.10476626. [Google Scholar]
- Donoho, D.L., and J.M. Johnstone. Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (3), 425–455, 1994, DOI: 10.1093/biomet/81.3.425. [CrossRef] [MathSciNet] [Google Scholar]
- Dupé, F.-X., J. Fadili, and J.-L. Starck. A proximal iteration for deconvolving Poisson noisy images using sparse representations. IEEE Trans. Image Process., 18 (2), 310–321, 2009, DOI: 10.1109/TIP.2008.2008223. [NASA ADS] [CrossRef] [Google Scholar]
- Elad, M., M. Figueiredo, and Y. Ma. On the role of sparse and redundant representations in image processing. Proc. IEEE, 98 (6), 972–982, 2010, DOI: 10.1109/JPROC.2009.2037655. [CrossRef] [Google Scholar]
- Fergus, R., B. Singh, A. Hertzmann, S.T. Roweis, and W.T. Freeman. Removing camera shake from a single photograph. ACM Trans. Graph., 25 (3), 787–794, 2006, DOI: 10.1145/1141911.1141956. [Google Scholar]
- Fish, D.A., J.G. Walker, A.M. Brinicombe, and E.R. Pike. Blind deconvolution by means of the Richardson-Lucy algorithm. J. Opt. Soc. Am. A, 12 (1), 58–65, 1995, DOI: 10.1364/JOSAA.12.000058. [Google Scholar]
- Gburek, S., J. Sylwester, and P. Martens. The trace telescope point spread function for the 171 Å filter. Sol. Phys., 239, 531–548, 2006, DOI: 10.1007/s11207-006-1994-0. [NASA ADS] [CrossRef] [Google Scholar]
- González, A., L. Jacques, C.D. Vleeschouwer, and P. Antoine. Compressive optical deflectometric tomography: a constrained total-variation minimization approach. Inverse Prob. Imaging, 8 (2), 421–457, 2014, DOI: 10.3934/ipi.2014.8.421. [CrossRef] [Google Scholar]
- Gray, R.M. Toeplitz and circulant matrices: a review. Found. Trends Commun. Inf. Theory, 2 (3), 155–239, 2006, DOI: 10.1561/0100000006. [CrossRef] [Google Scholar]
- Grigis, P., S. Yingna, and M. Weber. AIA PSF characterization and image deconvolution. Tech. Rep., AIA team, 2013. [Google Scholar]
- Hoeffding, W. Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc., 58 (301), 13–30, 1963, DOI: 10.1080/01621459.1963.10500830. [Google Scholar]
- Howard, R.A., J.D. Moses, A. Vourlidas, J.S. Newmark, D.G. Socker, et al. Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI). Space Sci. Rev., 136, 67–115, 2008, DOI: 10.1007/s11214-008-9341-4. [NASA ADS] [CrossRef] [Google Scholar]
- Jefferies, S., K. Schulze, C. Matson, K. Stoltenberg, and E.K. Hege. Blind deconvolution in optical diffusion tomography. Opt. Express, 10 (1), 46–53, 2002, DOI: 10.1364/OE.10.000046. [CrossRef] [Google Scholar]
- Lemen, J.R., A.M. Title, D.J. Akin, P.F. Boerner, and C. Chou. The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Sol. Phys., 275 (1–2), 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef] [Google Scholar]
- Mallat, S. Wavelet Tour of Signal Processing, Third Edition: The Sparse Way, 3rd edn., Academic Press, ISBN: 0123743702, 9780123743701, 2008. [Google Scholar]
- Martens, P.C., L.W. Acton, and J.R. Lemen. The point spread function of the soft X-ray telescope aboard YOHKOH. Sol. Phys., 157 (1–2), 141–168, 1995, DOI: 10.1007/BF00680614. [NASA ADS] [CrossRef] [Google Scholar]
- Meftah, M., J.-F. Hochedez, A. Irbah, A. Hauchecorne, P. Boumier, et al. Picard SODISM, a space telescope to study the sun from the middle ultraviolet to the near infrared. Sol. Phys., 289 (3), 1043–1076, 2014, DOI: 10.1007/s11207-013-0373-x. [Google Scholar]
- Nightingale, R.W. AIA/SDO FITS keywords for scientific usage and data processing at levels 0, 1.0, and 1.5. Tech. Rep., Lockheed-Martin Solar and Astrophysics Laboratory (LMSAL), 2011. [Google Scholar]
- Oliveira, J.A.P., M.A.T. Figueiredo, and J.M. Bioucas-Dias. Blind estimation of motion blur parameters for image deconvolution. In: J., Mart, J. Bened, A. Mendona, and J. Serrat, Editors. Pattern Recognition and Image Analysis, vol. 4478 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 604–611, ISBN: 978-3-540-72848-1, 2007, DOI: 10.1007/978-3-540-72849-8_76. [Google Scholar]
- Parikh, N., and S. Boyd. Proximal algorithms. Found. Trends Optim., 1 (3), 127–239, 2014, DOI: 10.1561/2400000003. [CrossRef] [MathSciNet] [Google Scholar]
- Pence, W., R. Seaman, and R. White. A comparison of lossless image compression methods and the effects of noise. In: Astronomical Data Analysis Software and Systems XVIII, ASP Conference Series, Vol. 411, 25, 2009. [Google Scholar]
- Pesnell, W.D., B.J. Thompson, and P.C. Chamberlin. The Solar Dynamics Observatory (SDO). Sol. Phys., 275 (1–2), 3–15, 2012, DOI: 10.1007/s11207-011-9841-3. [NASA ADS] [CrossRef] [Google Scholar]
- Poduval, B., C.E. DeForest, J.T. Schmelz, and S. Pathak. Point-spread functions for the extreme-ultraviolet channels of SDO/AIA Telescopes. Astrophys. J., 765 (2), 144, 2013, DOI: 10.1088/0004-637X/765/2/144. [Google Scholar]
- Prato, M., A.L. Camera, S. Bonettini, and M. Bertero. A convergent blind deconvolution method for postadaptive-optics astronomical imaging. Inverse Prob., 29 (6), 065017, 2013, , DOI: 0266-5611-29-6-065017. [Google Scholar]
- Prato, M., R. Cavicchioli, L. Zanni, P. Boccacci, and M. Bertero. Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes. A&A, 539, A133, 2012, DOI: 10.1051/0004-6361/201118681. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Puy, G., and P. Vandergheynst. Robust image reconstruction from multiview measurements. SIAM J. Imaging Sci., 7 (1), 128–156, 2014, DOI: 10.1137/120902586. [CrossRef] [Google Scholar]
- Raguet, H., J. Fadili, and G. Peyré. A generalized forward-backward splitting. SIAM J. Imaging Sci., 6 (3), 1199–1226, 2013, DOI: 10.1137/120872802. [CrossRef] [MathSciNet] [Google Scholar]
- Seaton, D.B., A. Degroof, P. Shearer, D. Berghmans, and B. Nicula. SWAP observations of the long-term, large-scale evolution of the extreme-ultraviolet solar corona. Astrophys. J, 777 (1), 72, 2013, DOI: 10.1088/0004-637X/777/1/72. [Google Scholar]
- Shearer, P., R.A. Frazin, A.O. Hero III, and A.C. Gilbert. The first stray light corrected extreme ultraviolet images of solar coronal holes. Astrophys. J., 749, L8, 2012, DOI: 10.1088/2041-8205/749/1/L8. [Google Scholar]
- Shearer, P.R. Separable inverse problems, blind deconvolution, and stray light correction for extreme ultraviolet solar images. Ph.D. thesis, The University of Michigan, 2013. [Google Scholar]
- Starck, J.-L., M. Elad, D. Donoho. Redundant multiscale transforms and their application for morphological component separation. Adv. Imaging Electron Phys., 132, 287–348, 2004, DOI: 10.1016/S1076-5670(04)32006-9. [Google Scholar]
- Starck, J.-L. and M. Fadili. An overview of inverse problem regularization using sparsity. Image Processing (ICIP), 2009 16th IEEE International Conference on, Cairo, 1453–1456, 2009, DOI: 10.1109/ICIP.2009.5414556. [Google Scholar]
- Starck, J.-L., and F. Murtagh. Image restoration with noise suppression using the wavelet transform. A&A, 288 (1), 342–348, 1994. [Google Scholar]
- Starck, J.-L., F. Murtagh, and J. Fadili. Sparse image and signal processing: wavelets, curvelets, morphological diversity, Cambridge University Press, New York, NY, USA, ISBN: 0521119138, 9780521119139, 2010. [CrossRef] [Google Scholar]
- Stein, C.M. Estimation of the mean of a multivariate normal distribution. Ann. Stat., 9 (6), 1135–1151, 1981, DOI: 10.1214/aos/1176345632. [CrossRef] [MathSciNet] [Google Scholar]
- Sudhakar, P., L. Jacques, X. Dubois, P. Antoine, and L. Joannes. Compressive imaging and characterization of sparse light deflection maps. SIAM J. Imaging Sci., 8 (3), 1824–1856, 2015, DOI: 10.1137/140974341. [CrossRef] [Google Scholar]
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