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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 47, Iss. 10 — Apr. 1, 2008
  • pp: B117–B127

Adaptive framework for robust high-resolution image reconstruction in multiplexed computational imaging architectures

Noha A. El-Yamany, Panos E. Papamichalis, and Marc P. Christensen  »View Author Affiliations


Applied Optics, Vol. 47, Issue 10, pp. B117-B127 (2008)
http://dx.doi.org/10.1364/AO.47.00B117


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Abstract

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.

© 2008 Optical Society of America

OCIS Codes
(110.1758) Imaging systems : Computational imaging

ToC Category:
Imaging Systems

History
Original Manuscript: September 11, 2007
Revised Manuscript: January 23, 2008
Manuscript Accepted: February 25, 2008
Published: April 1, 2008

Citation
Noha A. El-Yamany, Panos E. Papamichalis, and Marc P. Christensen, "Adaptive framework for robust high-resolution image reconstruction in multiplexed computational imaging architectures," Appl. Opt. 47, B117-B127 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-10-B117


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References

  1. J. N. Mait, R. Athale, and J. van der Gracht, “Evolutionary paths in imaging and recent trends,” Opt. Express 11, 2093-2101 (2003). [CrossRef] [PubMed]
  2. J. Mait, M. W. Haney, Keith Goossen, and M. P. Christensen, “Shedding light on the battlefield: tactical applications of photonic technology,” Ref. A370034 (National Defense University Center for Technology and National Security Policy, 2004).
  3. P. M. Shankar, W. C. Hasenplaugh, R. L. Morrison, R. A. Stack, and M. A. Neifeld, “Multiaperture imaging,” Appl. Opt. 45, 2871-2883 (2006). [CrossRef] [PubMed]
  4. J. Tanida, T. Kumagai, K. Yamada, and S. Miyatake, “Thin observation module by bound optics (TOMBO): concept and experimental verification,” Appl. Opt. 40, 1806-1813 (2001). [CrossRef]
  5. M. P. Christensen, M. W. Haney, D. Rajan, S. Wood, and S. Douglas, “PANOPTES: a thin agile multi-resolutions imaging sensor,” presented at the Government Microcircuit Applications and Critical Technology Conference (GOMACTech-05), Las Vegas, Nevada, 4-7 April 2005, paper 21.5.
  6. M. W. Haney, M. P. Christensen, D. Rajan, S. C. Douglas, and S. L. Wood, “Adaptive flat micro-mirror-based computational imaging architecture,” presented at OSA Topical Meeting on Computational Optical Sensing and Imaging (COSI), Charlotte, North Carolina, 6-9 June 2005.
  7. M. P. Christensen, V. Bhakta, D. Rajan, T. Mirani, S. C. Douglas, S. L. Wood, and M. W. Haney, “Adaptive flat multiresolution multiplexed computational imaging architecture utilizing micromirror arrays to steer subimager fields of view,” Appl. Opt. 45, 2884-2892 (2006). [CrossRef] [PubMed]
  8. H.-B. Lan, S. L. Wood, M. P. Christensen, and D. Rajan, “Benefits of optical system diversity for multiplexed image reconstruction,” Appl. Opt. 45, 2859-2870 (2006). [CrossRef] [PubMed]
  9. K. Aizawa, T. Komatsu, and T. Saito, “A scheme for acquiring very high resolution images using multiple cameras,” IEEE Trans. Acoust. Speech Signal Process. 3, 23-26 (1992).
  10. S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20 (3), 21-36 (2003). [CrossRef]
  11. S. Chaudhuri, Super-Resolution Imaging (Norwell, 2001).
  12. R. Y. Tsai and T. S. Huang, “Multiframe image restoration and registration,” in Advances in Computer Vision and Image Processing, T. S. Huang, ed. (JAI Press, 1984), Vol. 1, pp. 317-339.
  13. H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” CVGIP Graph. Models Image Process. 54, 181-186 (1992). [CrossRef]
  14. M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231-239(1991). [CrossRef]
  15. R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, and E. A. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247-260 (1998). [CrossRef]
  16. M. Elad and A. Feuer, “Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images,” IEEE Trans. Image Process. 6, 1646-1658(1997). [CrossRef]
  17. M. Elad and Y. Hel-Or, “A fast super-resolution reconstruction algorithm for pure translation motion and common space- invariant blur,” IEEE Trans. Image Process. 10, 1187-1193(2001). [CrossRef]
  18. A. Zomet and S. Peleg, “Efficient super-resolution and applications to mosaics,” in 15th International Conference on Pattern Recognition, 2000 (2000), Vol. 1, pp. 579-583,. [CrossRef]
  19. A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, Vol. 1, I-645-I-650 (2001). [CrossRef]
  20. N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001). [CrossRef]
  21. S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).
  22. S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multi-frame super-resolution,” IEEE Trans. Image Process. 13,1327-1344 (2004). [CrossRef]
  23. E. S. Lee and M. G. Kang, “Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration,” IEEE Trans. Image Process. 12, 826-837 (2003). [CrossRef]
  24. M. V. W. Zibetti and J. Mayer, “Outlier robust and edge-preserving simultaneous super-resolution,” in IEEE International Conference on Image Processing, 1741 -1744 (2006). [CrossRef]
  25. M. Trimeche, R. C. Bilcu, and J. Yrjänäinen, “Adaptive outlier rejection in image super-resolution”, EURASIP J. Appl. Signal Process. 2006, 38052 (2006). [CrossRef]
  26. D. Capel, Image Mosaicing and Super-resolution (Springer, 2004). [CrossRef]
  27. V. Patanavijit and S. Jitapunkul, “A Lorentzian stochastic estimation for a robust iterative multiframe super-resolution reconstruction with Lorentzian-Tikhonov regularization,” EURASIP J. Adv. Signal Process. 2007, 34821 (2007). [CrossRef]
  28. N. A. El-Yamany and P. E. Papamichalis are preparing a manuscript to be called “Using bounded-influence M-estimators in multiframe super-resolution reconstruction: a comparative study.”
  29. N. A. El-Yamany and P. E. Papamichalis, “An adaptive M-estimation framework for robust image super-resolution without regularization,” to appear in SPIE Conference on Visual Communications and Image Processing (VCIP), San Jose, California, 2008.
  30. N. A. El-Yamany, P. E. Papamichalis, and W. R. Schucany, “A robust image super-resolution scheme based on redescending M-estimators and information-theoretic divergence,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honolulu, Hawaii (2007).
  31. N. A. El-Yamany and P. E. Papamichalis, “Robust color image super-resolution: an adaptive M-estimation framework,” EURASIP J. Image Video Process. (2008). [CrossRef]
  32. T. Q. Pham, L. J. van Vliet, and K. Schutte, “Robust super-resolution by minimizing a Gaussian-weighted L2 error norm,” J. Phys. Conf. Ser. , to be published.
  33. Z. A. Ivanovski, L. Panovski, and L. J. Karam, “Robust super-resolution based on pixel-level selectivity”, Proc. SPIE 6077, 607707 (2006). [CrossRef]
  34. W. Zhao and H. S. Sawhney, “Is super-resolution with optical flow feasible?,” in Proceedings of the 7th European Conference on Computer Vision-Part I, A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, eds. (Springer-Verlag, 2002), pp. 599-613.
  35. P. J. Huber, Robust Statistics, Wiley Series in Probability and Statistics (Wiley-Interscience, 2003).
  36. F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust Statistics: the Approach Based on Influence Functions, Wiley Series in Probability and Statistics (Wiley-Interscience, 2005). [CrossRef]
  37. R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, Wiley Series in Probability and Statistics (Wiley, 2006). [CrossRef]
  38. N. Sebe and M. S. Lew, Robust Computer Vision: Theory and Applications (Springer, 2003).
  39. P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim, “Robust regression methods for computer vision: a review,” Int. J. Comput. Vision 6, 59-70 (1991). [CrossRef]
  40. M. J. Black and P. Anandan, “The robust estimation of multiple motions: parametric and piecewise-smooth flow fields,” Comput. Vision Image Understand. 63(1), 75-104 (1996). [CrossRef]
  41. T. Rabie, “Robust estimation approach for blind denoising,” IEEE Trans. Image Process. 14, 1755-1765 (2005). [CrossRef]
  42. M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998). [CrossRef]
  43. D. P. Bertsekas, Nonlinear Programming (Athena Scientific, 1999).
  44. A. Blake and A. Zisserman, Visual Reconstruction (MIT Press, 1987).
  45. J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani, “Hierarchical model-based motion estimation,” in Proceedings of the European Conference on Computer Vision (Springer-Verlag, 1992), pp. 237-252.

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