Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Adriana González; Véronique Delouille; Laurent Jacques

doi:10.1051/swsc/2015040

All issues

Volume 6 (2016)

J. Space Weather Space Clim., 6 (2016) A1

References

Statistical Challenges in Solar Information Processing

Open Access

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. [CrossRef] [MathSciNet] [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. [NASA ADS] [CrossRef] [MathSciNet] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [MathSciNet] [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. [NASA ADS] [CrossRef] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [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. [NASA ADS] [CrossRef] [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. [CrossRef] [MathSciNet] [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. [NASA ADS] [CrossRef] [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. [NASA ADS] [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [NASA ADS] [CrossRef] [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. [CrossRef] [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.