Interferometric phase image estimation via sparse coding in the complex domain

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 5, 2015, Pages 2587-2602

  Hongxing, Hao ^a,b   Bioucas Dias, Jose M ^b   Katkovnik, Vladimir ^c  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Dictionary learning (DL); image similarity; interferometric phase estimation; online learning; phase estimation; phase unwrapping; sparse regression

Indexed keywords

CODES (SYMBOLS); DEGRADATION; IMAGE RECONSTRUCTION; INTERFEROMETRY; OPTIMAL SYSTEMS; REGRESSION ANALYSIS;

DICTIONARY LEARNING; IMAGE SIMILARITY; INTERFEROMETRIC PHASE; ONLINE LEARNING; PHASE ESTIMATION; PHASE UNWRAPPING; SPARSE REGRESSION;

IMAGE CODING;

ESTIMATION METHOD; IMAGE ANALYSIS; IMAGE PROCESSING; INTERFEROMETRY; MATRIX; NOISE; NUMERICAL MODEL; REGRESSION ANALYSIS; VECTOR;

EID: 84921031967     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2361919     Document Type: Article

References (59)

1. L. Graham, "Synthetic interferometer radar for topographic mapping" Proc. IEEE, vol. 62, no. 6, pp. 763-768, Jun. 1974
2. H. Zebker and R. Goldstein, "Topographic mapping from interferometric synthetic aperture radar observations" J. Geophys. Res., vol. 91, no. B5, pp. 4993-4999, Apr. 1986
3. P. Lauterbur, "Image formation by induced local interactions: Examples employing nuclear magnetic resonance" Nature, vol. 242, no. 5394, pp. 190-191, Mar. 1973
4. M. Hedley and D. Rosenfeld, "A new two-dimensional phase unwrapping algorithm for MRI images" Magn. Reson. Med., vol. 24, no. 1, pp. 177-181, Mar. 1992
5. D. Ghiglia and M. Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. Hoboken, NJ, USA: Wiley, 1998
6. T. Kreis, Handbook of Holographic Interferometry: Optical and Digital Methods. Weinheim, Germany: Wiley-VCH, 2005
7. A. Patil and P. Rastogi, "Moving ahead with phase" Opt. Lasers Eng., vol. 45, no. 2, pp. 253-257, Feb. 2007
8. N. Karpinsky and S. Zhang, "High-resolution, real-time 3D imaging with fringe analysis" J. Real-Time Image Process., vol. 7, no. 1, pp. 55-66, Mar. 2012
9. C. David, B. Nohammer, H. Solak, and E. Ziegler, "Differential X-ray phase contrast imaging using a shearing interferometer" Appl. Phys. Lett., vol. 81, no. 17, pp. 3287-3289, Oct. 2002
10. V. Katkovnik, K. Egiazarian, and J. Astola, Local Approximation Techniques in Signal and Image Processing. Bellingham, WA, USA: SPIE, 2006, ser. SPIE field guides
11. Q. Kemao, "Two-dimensional windowed Fourier transform for fringe pattern analysis: Principles, applications and implementations" Opt. Lasers Eng., vol. 45, no. 2, pp. 304-317, Feb. 2007
12. Q. Kemao, L. Nam, L. Feng, and S. Soon, "Comparative analysis on some filters for wrapped phase maps" Appl. Opt., vol. 46, no. 30, pp. 7412-7418, Oct. 2007
13. J. Bioucas-Dias, V. Katkovnik, J. Astola, and K. Egiazarian, "Absolute phase estimation: Adaptive local denoising and global unwrapping" Appl. Opt., vol. 47, no. 29, pp. 5358-5369, Oct. 2008
14. J. Bioucas-Dias and G. Valadão, "Phase unwrapping via graph cuts" IEEE Trans. Image Process., vol. 16, no. 3, pp. 698-709, Mar. 2007
15. C.-A. Deledalle, L. Denis, and F. Tupin, "NL-InSAR: Non-local interferogram estimation" IEEE Trans. Geosci. Remote Sens., vol. 49, no. 4, pp. 1441-1452, Apr. 2011
16. A. Efros and T. Leung, "Texture synthesis by non-parametric sampling" in Proc. 7th IEEE Int. Conf. Comput. Vis., 1999, vol. 2, pp. 1033-1038
17. A. Buades, B. Coll, and J. Morel, "A non-local algorithm for image denoising" in Proc. IEEE CVPR, 2005, vol. 2, pp. 60-65
18. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3D transform-domain collaborative filtering" IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, Aug. 2007
19. J. Bioucas-Dias and J. Leitão, "The Z?M algorithm: A method for interferometric image reconstruction in SAR/SAS" IEEE Trans. Image Process., vol. 11, no. 4, pp. 408-422, Apr. 2002
20. V. Katkovnik, J. Astola, and K. Egiazarian, "Phase local approximation (PhaseLa) technique for phase unwrap from noisy data" IEEE Trans. Image Process., vol. 17, no. 6, pp. 833-846, Jun. 2008
21. G. Nico and J. Fortuny-Guasch, "Using the matrix pencil method to solve phase unwrapping" IEEE Trans. Signal Process., vol. 51, no. 3, pp. 886-888, Mar. 2003
22. M. Rivera and J. Marroquin, "Half-quadratic cost functions for phase unwrapping" Opt. Lett., vol. 29, no. 5, pp. 504-506, Mar. 2004
23. G. Valadão and J. Bioucas-Dias, "CAPE: Combinatorial absolute phase estimation" J. Opt. Soc. Amer. A, Opt. Image Sci., vol. 26, no. 9, pp. 2093-2106, Sep. 2009
24. J. Leitão and M. Figueiredo, "Absolute phase image reconstruction: A stochastic nonlinear filtering approach" IEEE Trans. Image Process., vol. 7, no. 6, pp. 868-882, Jun. 1998
25. O. Loffeld, H. Nies, S. Knedlik, and Y. Wang, "Phase unwrapping for SAR interferometry-A data fusion approach by Kalman filtering" IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 47-58, Jan. 2008
26. B. Osmanoglu, T. Dixon, and S. Wdowinski, "Three-dimensional phase unwrapping for satellite radar interferometry, I: DEM generation" IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1059-1075, Feb. 2014
27. J. Martinez-Espla, T. Martinez-Marin, and J. Lopez-Sanchez, "Using a grid-based filter to solve InSAR phase unwrapping" IEEE Trans. Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 147-151, Apr. 2008
28. J. Martinez-Espla, T. Martinez-Marin, and J. Lopez-Sanchez, "A particle filter approach for InSAR phase filtering and unwrapping" IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1197-1211, Apr. 2009
29. J. Martinez-Espla, T. Martinez-Marin, and J. Lopez-Sanchez, "An optimized algorithm for InSAR phase unwrapping based on particle filtering, matrix pencil, and region-growing techniques" IEEE Trans. Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 835-839, Oct. 2009
30. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries" IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006
31. M. Elad, Sparse and Redundant Representations. Berlin, Germany: Springer-Verlag, 2010
32. J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration" IEEE Trans. Image Process., vol. 17, no. 1, pp. 53-69, Jan. 2008
33. R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng, "Self-taught learning: Transfer learning from unlabeled data" in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 759-766
34. J. Mairal, F. Bach, and J. Ponce, "Task-driven dictionary learning" IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 791-804, Apr. 2012
35. S. Chen, D. Donoho, M. Saunders, and A. Michael, "Atomic decomposition by basis pursuit" SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001
36. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online dictionary learning for sparse coding" in Proc. 26th Annu. ICML, 2009, pp. 689-696
37. M. Afonso, J. Bioucas-Dias, and M. Figueiredo, "Fast image recovery using variable splitting and constrained optimization" IEEE Trans. Image Process., vol. 19, no. 9, pp. 2345-2356, Sep. 2010
38. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression" Ann. Stat., vol. 32, no. 2, pp. 407-499, 2004
39. R. Bamler and P. Hartl, "Synthetic aperture radar interferometry" Inverse Probl., vol. 14, no. 4, pp. R1-R54, Aug. 1998
40. S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, vol. I. Englewood Cliffs, NJ, USA: Prentice-Hall, 1993
41. M. Elad, "Sparse and redundant representation modeling-What next?" IEEE Signal Process. Lett., vol. 19, no. 12, pp. 922-928, Dec. 2012
42. J. Mairal et al., "Non-local sparse models for image restoration" in Proc. IEEE 12th Int. Conf. Comput. Vis., 2009, pp. 2272-2279
43. K. Natarajan, "Sparse approximate solutions to linear systems" SIAM J. Comput., vol. 24, no. 2, pp. 227-234, Apr. 1995
44. D. Just and R. Bamler, "Phase statistics of interferograms with applications to synthetic aperture radar" Appl. Opt., vol. 33, no. 20, pp. 4361-4368, Jul. 1994
45. R. Tibshirani, "Regression shrinkage and selection via the LASSO" J. R. Stat. Soc. Ser. B, vol. 58, no. 1, pp. 267-288, 1996
46. Y. Pati, R. Rezaiifar, and P. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition" in Conf. Rec. 27th Asilomar Conf. Signals, Syst. Comput., 1993, vol. 1, pp. 40-44
47. T. Blumensath andM. Davies, "Iterative hard thresholding for compressed sensing" Appl. Comput. Harmon. Anal., vol. 27, no. 3, pp. 265-274, Nov. 2009
48. S. Foucart, "Hard thresholding pursuit: An algorithm for compressive sensing" SIAM J. Numer. Anal., vol. 49, no. 6, pp. 2543-2563, Nov. 2011
49. J. Vila and P. Schniter, "Expectation-maximization Bernoulli-Gaussian approximate message passing" in Conf. Rec. 45th ASILOMAR Conf. Signals, Syst. Comput., 2011, pp. 799-803
50. D. Donoho and M. Elad, "Optimal sparse representation in general (nonorthogonal) dictionaries via-1 minimization" Proc. Nat. Acad. Sci. USA, vol. 100, no. 5, pp. 2197-2202, Mar. 2003
51. E. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements" Commun. Pure Appl. Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006
52. A. Olshausen and D. Field, "Sparse coding with an overcomplete basis set: A strategy employed by V1" Vis. Res., vol. 37, no. 23, pp. 3311-3326, Dec. 1997
53. M. Lewicki and T. Sejnowski, "Learning overcomplete representations" Neural Comput., vol. 12, no. 2, pp. 337-365, Feb. 2000
54. M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation" IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006
55. H. Lee, A. Battle, R. Raina, and A. Ng, "Efficient sparse coding algorithms" in Proc. Adv. Neural Inf. Process. Syst., 2007, vol. 19, pp. 801-808
56. A. Charles, B. Olshausen, and C. Rozell, "Learning sparse codes for hyperspectral imagery" IEEE J. Sel. Topics Signal Process., vol. 5, no. 5, pp. 963-978, Sep. 2011
57. A. Rakotomamonjy, "Applying alternating direction method of multipliers for constrained dictionary learning" Neurocomputing, vol. 106, pp. 126-136, Apr. 2012
58. J. Bioucas-Dias and M. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing" in Proc. 2nd WHISPERS, 2010, pp. 1-4
59. S. Boyd, N. Parikh, E. Chu, B. Peleato, and E. Jonathan, "Distributed optimization and statistical learning via the alternating direction method of multipliers" Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, Jan. 2011