Signal-processing approaches for image-resolution restoration for TOMBO imagery

Applied Optics

Volumn 47, Issue 10, 2008, Pages

  Choi, Kerkil ^a   Schulz, Timothy J ^a  

^a Michigan Technological University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; IMAGE RESOLUTION; IMAGE SEGMENTATION; INFORMATION THEORY; ITERATIVE METHODS; MAXIMUM PRINCIPLE; OPTICAL SYSTEMS; OPTIMIZATION; RESTORATION; SIGNAL PROCESSING;

ADVANCED SIGNAL PROCESSING; EXPECTATION-MAXIMIZATION METHOD; HIGH RESOLUTION IMAGE; INFORMATION THEORETIC MEASURE; MEASUREMENT SELECTIONS; NON-NEGATIVITY CONSTRAINTS; OPTIMIZATION PROBLEMS; RESTORATION ALGORITHM;

GAUSSIAN NOISE (ELECTRONIC);

EID: 48949106447     PISSN: 1559128X     EISSN: 15394522     Source Type: Journal    
DOI: 10.1364/AO.47.00B104     Document Type: Article

References (66)

1. J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Morimoto, N. Kondou, D. Miyazaki, and Y. Ichioka, "Thin observation module by bound optics (TOMBO): concept and experimental verification," Appl. Opt. 40, 1806-1813 (2001).
2. A. V. Kanaev, J. R. Ackerman, E. F. Fleet, and D. A. Scribner, "Compact TOMBO sensor with scene-independent super-resolution processing," in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, OSA Technical Digest (CD) (Optical Society of America, 2007), paper CMA3.
3. K. Choi and T. J. Schulz, "Superresolution for thin optics," in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, OSA Technical Digest (CD) (Optical Society of America, 2007), paper CMA4.
4. M. E. Testorf, J. Carter, M. A. Fiddy, and T. J. Suleski, "Multi-aperture diversity imaging: Physical limitations to the generalized sampling theorem (GST)," in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, OSA Technical Digest (CD) (Optical Society of America, 2007), paper CMA5.
5. D. J. Brady, M. A. Fiddy, U. Shahid, and T. J. Suleski, "Compressive optical MONTAGE photography initiative: noise and error analysis," in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, OSA Technical Digest (Optical Society of America, 2005), paper CMB3.
6. N. P. Pitsianis, D. J. Brady, and X. Sun, "The MONTAGE least gradient image reconstruction," in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, OSA Technical Digest (CD) (Optical Society of America, 2007), paper CTuB3.
7. Y. Vardi and D. Lee, "From image deblurring to optimal investments: Maximum likelihood solutions for positive linear inverse problems," J. R. Stat. Soc. B 55, 569-612 (1993).
8. L. A. Shepp and Y. Vardi, "Maximum likelihood reconstruction in positron emission tomography," IEEE Trans. Med Imaging 1, 113-122 (1982).
9. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. B 39, 1-38 (1977).
10. J. A. O'Sullivan and D. L. Snyder, "Deterministic EM algorithms with penalties," in Proceedings of IEEE International Symposium on Information Theory (IEEE, 1995), p. 177.
11. I. Csiszar, "Why least squares and maximum entropy - An axiomatic approach to inference for linear inverse problems," Ann. Stat. 19, 2032-2066 (1991).
12. I. Csiszar and L. Finesso, "On a problem about I-projections, " in Proceedings of IEEE International Symposium on Information Theory (IEEE, 1997), p. 279.
13. B. W. Silverman, M. C. Jones, J. D. Wilson, and D. W. Nychka, "A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography," J. R. Stat. Soc. B 52, 271-324 (1990).
14. V. Y. Panin, G. L. Zeng, and G. T. Gullberg, "Total variation regulated EM algorithm," in Proceedings of IEEE International Symposium on Nuclear Science (IEEE, 1988), pp. 1562-1566.
15. T. Chan, A. Marquina, and P. Mulet, "High-order total variation-based image restoration," SIAM J. Sci. Comput. 22, 503-516 (2000).
16. T. Chan and J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods, 1st ed. (Society for Industrial and Applied Mathematics, 2005).
17. A. Bouhamidi and K. Jbilou, "Sylvester Tikhonov-regularization methods," J. Comput. Appl. Math. 206, 86-98 (2007).
18. I. J. Good and R. A. Gaskins, "Nonparametric roughness penalties for probability densities," Biometrika 58, 255-277 (1971).
19. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory 52, 489-509 (2006).
20. D. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory 52, 1289-1306 (2006).
21. E. Candes, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure Appl. Math. 59, 1207-1223 (2006).
22. J. Haupt and R. Nowak, "Signal reconstruction from noisy random projections," IEEE Trans. Inf. Theory 52, 4036-4048 (2006).
23. P. Blomgren, T. F. Chan, P. Mulet, and C. K. Wong, "Total variation image restoration: numerical methods and extensions," in Proceedings of International Conference on Image Processing (IEEE, 1997), pp. 384-387.
24. T. F. Chan, G. H. Golub, and P. Mulet, "A nonlinear primaldual method for total variation-based image restoration," SIAM J. Sci. Comput. 20, 1964-1977 (1999).
25. D. Strong and T. Chan, "Edge-preserving and scale-dependent properties of total variation regularization," Inverse Probl. 19, S165-S187 (2003).
26. J. A. Fessler and A. O. Hero, "Space-alternating generalized expectation-maximization algorithm," IEEE Trans. Signal Process. 42, 2664-2677 (1994).
27. J. A. Fessler and A. O. Hero, "Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms," IEEE Trans. Image Process. 4, 1417-1429 (1995).
28. P. J. Green, "On use of the EM for penalized likelihood estimation," J. R. Stat. Soc. B 52, 443-452 (1990).
29. P. J. Green, "Bayesian reconstruction from emission tomography data using a modified EM algorithm," IEEE Trans. Med. Imaging 9, 84-93 (1990).
30. B. A. Mair and J. Zahnen, "A generalization of Green's one-step-late algorithm for penalized ML reconstruction of PET images," in Proceedings of International Symposium on Nuclear Science (IEEE, 2006), pp. 2775-2777.
31. J. W. Goodman, Introduction to Fourier Optics, 3rd ed. (Roberts & Company, 2005).
32. S. Baker and T. Kanade, "Limits on super-resolution and how to break them," IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002).
33. B. Bascle, A. Blake, and A. Zisserman, "Motion deblurring and super-resolution from an image sequence," in Computer Vision - ECCV '96 (Springer,1996), pp. 573-581.
34. M. Elad, "Super-resolution reconstruction of image sequences - adaptive filtering approach," Ph.D. thesis (The Technion Israel Institute of Technology, 1996).
35. M. Elad and A. Feuer, "Restoration of single super-resolution image from several blurred, noisy, and down-sampled measured images," IEEE Trans. Image Process. 6, 1646-1658 (1997).
36. M. Elad and A. Feuer, "Super-resolution reconstruction of image sequences," IEEE Trans. Pattern Anal. Mach. Intell. 21, 817-834 (1999).
37. M. Elad and A. Feuer, "super-resolution restoration of an image sequence - adaptive filtering approach," IEEE Trans. Image Process. 8, 387-395 (1999).
38. S. Kim, N. Bose, and H. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Trans. Acoust. Speech Signal Process. 38, 1013-1027 (1990).
39. S. Kim and W.-Y. Su, "Recursive high-resolution reconstruction of blurred multiframe images," IEEE Trans. Image Process. 2, 534-539 (1993).
40. G. K. Chantas, N. P. Galatsanos, and N. A. Woods, "Super-resolution based on fast registration and maximum a posteriori reconstruction," IEEE Trans. Image Process. 16, 1821-1830 (2007).
41. S. Farsiu, M. Elad, and P. Milanfar, "Multiframe demosaicing and super-resolution of color images," IEEE Trans. Image Process. 15, 141-159 (2006).
42. S. Park, M. Park, and M. G. Kang, "Super-resolution image reconstruction, a technical overview," IEEE Signal Process. Mag. 20, 21-36 (2003).
43. D. L. Snyder, A. M. Hammoud, and R. L. White, "Image recovery from data acquired with a charge-coupled-device camera," J. Opt. Soc. Am. A 10, 1014-1023 (1993).
44. L. G. Brown, "A survey of image registration techniques," ACM Comput. Surv. 24, 325-376 (1992).
45. W. Li and H. Leung, "A maximum likelihood approach for image registration using control point and intensity," IEEE Trans. Image Process. 13, 1115-1127 (2004).
46. J. B. A. Maintz and M. A. Viergever, "A survey of medical image registration," Med. Image Anal. 2, 1-36 (1998).
47. Y. Bresler and S. J. Merhav, "Recursive image registration with application to motion estimation," IEEE Trans. Acoust. Speech Signal Process. 35, 70-85 (1987).
48. D. L. Snyder, T. J. Schulz, and J. A. O'Sullivan, "Deblurring subject to nonnegativity constraints," IEEE Trans. Signal Process. 40, 1143-1150 (1992).
49. D. L. Donoho and J. Tanner, "Neighborliness of randomly projected simplices in high dimensions," in Proc. Natl. Acad. Sci. USA 102, 9452-9457 (2005).
50. S. Kullback and R. A. Leibler, "On information and sufficiency," Ann. Math. Stat. 22, 79-86 (1951).
51. J. Biemond, R. L. Lagendijk, and R. M. Mersereau, "Iterative methods for image deblurring," Proc. IEEE 78, 856-883 (1990).
52. M. R. Banham and A. K. Katsaggelos, "Digital image restoration," IEEE Signal Process. Mag. 14, 24-41 (1997).
53. D. L. Snyder, M. I. Miller, L. J. Thomas, and D. G. Politte, "Noise and edge artifacts in maximum-likelihood reconstructions for emission tomography," IEEE Trans. Med. Imaging MI-6, 228-238 (1987).
54. T. J. Schulz and D. L. Snyder, "Image recovery from correlations," J. Opt. Soc. Am. A 9, 1266-1272 (1992).
55. K. Choi, "Minimum I-divergence methods for inverse problems," Ph.D. thesis (Georgia Institute of Technology, 2005).
56. W. H. Richardson, "Bayesian-based iterative method of image restoration," J. Opt. Soc. Am. 62, 55-59 (1972).
57. R. H. Chan, T. F. Chan, and C. K. Wong, "Cosine transform based preconditioners for total variation deblurring," IEEE Trans. Image Process. 8, 1472-1478 (1999).
58. P. L. Combettes and J. Luo, "An adaptive level set method for nondifferentiable constrained image recovery," IEEE Trans. Image Process. 11, 1295-1304 (2002).
59. P. L. Combettes and J. C. Pesquet, "Image restoration subject to a total variation constraint," IEEE Trans. Image Process. 13, 1213-1222 (2004).
60. E. Jonsson, S. Huang, and T. Chan, "Total variation regularization in positron emission tomography," UCLA Computational and Applied Mathematics Rep. 98-48 (U. California Los Angeles, 1998).
61. T. F. Chan and C. K. Wong, "Total variation blind deconvolution," IEEE Trans. Image Process. 7, 370-375 (1998).
62. M. I. Miller and B. Roysam, "Bayesian image reconstruction for emission tomography incorporating Good's roughness prior on massively parallel processors," Proc. Natl. Acad. Sci. USA 88, 3223-3227 (1991).
63. S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. (Academic, 1999).
64. K. Lange, "Convergence of EM image reconstruction algorithms with Gibbs smoothing," IEEE Trans. Med. Imaging 9, 439-446 (1990).
65. G. H. Golub and U. von Matt, "Generalized cross-validation for large-scale problems," J. Comput. Graph. Stat. 6, 1-34 (1997).
66. M. Shankar, R. Willett, N. P. Pitsianis, R. Te Kolste, C. Chen, R. Gibbons, and D. J. Brady, "Ultra-thin multiple-channel LWIR imaging systems," Proc. SPIE. 6294, 629411 (2006).