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

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    [CrossRef]
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2008 (1)

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]

2007 (1)

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]

2006 (5)

2005 (1)

T. Rabie, “Robust estimation approach for blind denoising,” IEEE Trans. Image Process. 14, 1755-1765 (2005).
[CrossRef]

2004 (1)

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]

2003 (3)

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]

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]

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]

2001 (3)

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]

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]

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001).
[CrossRef]

1998 (2)

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]

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

1997 (1)

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]

1996 (1)

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]

1992 (2)

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).

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” CVGIP Graph. Models Image Process. 54, 181-186 (1992).
[CrossRef]

1991 (2)

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231-239(1991).
[CrossRef]

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]

Aizawa, K.

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).

Anandan, P.

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]

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.

Armstrong, E. E.

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]

Athale, R.

Barnard, K. J.

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]

Bergen, J. R.

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.

Bertsekas, D. P.

D. P. Bertsekas, Nonlinear Programming (Athena Scientific, 1999).

Bhakta, V.

Bilcu, R. C.

M. Trimeche, R. C. Bilcu, and J. Yrjänäinen, “Adaptive outlier rejection in image super-resolution”, EURASIP J. Appl. Signal Process. 2006, 38052 (2006).
[CrossRef]

Black, M. J.

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

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]

Blake, A.

A. Blake and A. Zisserman, Visual Reconstruction (MIT Press, 1987).

Bognar, J. G.

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]

Capel, D.

D. Capel, Image Mosaicing and Super-resolution (Springer, 2004).
[CrossRef]

Chaudhuri, S.

S. Chaudhuri, Super-Resolution Imaging (Norwell, 2001).

Christensen, M. P.

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]

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]

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.

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.

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).

Douglas, S.

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.

Douglas, S. C.

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]

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.

Elad, M.

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]

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]

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]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).

El-Yamany, N. A.

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]

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.

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.”

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).

Farsiu, S.

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]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).

Feuer, A.

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]

Golub, G.

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001).
[CrossRef]

Goossen, Keith

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).

Gross, D.

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” CVGIP Graph. Models Image Process. 54, 181-186 (1992).
[CrossRef]

Hampel, F. R.

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]

Haney, M. W.

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]

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.

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).

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.

Hanna, K. J.

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.

Hardie, R. C.

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]

Hasenplaugh, W. C.

Heeger, D.

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

Hel-Or, Y.

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]

Hingorani, R.

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.

Huang, T. S.

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.

Huber, P. J.

P. J. Huber, Robust Statistics, Wiley Series in Probability and Statistics (Wiley-Interscience, 2003).

Irani, M.

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231-239(1991).
[CrossRef]

Ivanovski, Z. A.

Z. A. Ivanovski, L. Panovski, and L. J. Karam, “Robust super-resolution based on pixel-level selectivity”, Proc. SPIE 6077, 607707 (2006).
[CrossRef]

Jitapunkul, S.

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]

Kang, M. G.

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]

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]

Karam, L. J.

Z. A. Ivanovski, L. Panovski, and L. J. Karam, “Robust super-resolution based on pixel-level selectivity”, Proc. SPIE 6077, 607707 (2006).
[CrossRef]

Kim, D. Y.

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]

Komatsu, T.

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).

Kumagai, T.

Lan, H.-B.

Lee, E. S.

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]

Lew, M. S.

N. Sebe and M. S. Lew, Robust Computer Vision: Theory and Applications (Springer, 2003).

Mait, J.

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).

Mait, J. N.

Marimont, D H.

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

Maronna, R. A.

R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, Wiley Series in Probability and Statistics (Wiley, 2006).
[CrossRef]

Martin, D. R.

R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, Wiley Series in Probability and Statistics (Wiley, 2006).
[CrossRef]

Mayer, J.

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]

Meer, P.

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]

Milanfar, P.

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]

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001).
[CrossRef]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).

Mintz, D.

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]

Mirani, T.

Miyatake, S.

Morrison, R. L.

Neifeld, M. A.

Nguyen, N.

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001).
[CrossRef]

Panovski, L.

Z. A. Ivanovski, L. Panovski, and L. J. Karam, “Robust super-resolution based on pixel-level selectivity”, Proc. SPIE 6077, 607707 (2006).
[CrossRef]

Papamichalis, P. E.

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]

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.

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.”

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).

Park, M. K.

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]

Park, S. C.

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]

Patanavijit, V.

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]

Peleg, S.

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231-239(1991).
[CrossRef]

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]

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]

Pham, T. Q.

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.

Rabie, T.

T. Rabie, “Robust estimation approach for blind denoising,” IEEE Trans. Image Process. 14, 1755-1765 (2005).
[CrossRef]

Rajan, D.

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]

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]

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.

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.

Rav-Acha, A.

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]

Robinson, D.

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]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).

Ronchetti, E. M.

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]

Rosenfeld, A.

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]

Rousseeuw, P. J.

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]

Saito, T.

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).

Sapiro, G.

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

Sawhney, H. S.

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.

Schucany, W. R.

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).

Schutte, K.

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.

Sebe, N.

N. Sebe and M. S. Lew, Robust Computer Vision: Theory and Applications (Springer, 2003).

Shankar, P. M.

Stack, R. A.

Stahel, W. A.

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]

Tanida, J.

Trimeche, M.

M. Trimeche, R. C. Bilcu, and J. Yrjänäinen, “Adaptive outlier rejection in image super-resolution”, EURASIP J. Appl. Signal Process. 2006, 38052 (2006).
[CrossRef]

Tsai, R. Y.

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.

Ur, H.

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” CVGIP Graph. Models Image Process. 54, 181-186 (1992).
[CrossRef]

van der Gracht, J.

van Vliet, L. J.

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.

Watson, E. A.

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]

Wood, S.

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.

Wood, S. L.

Yamada, K.

Yohai, V. J.

R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, Wiley Series in Probability and Statistics (Wiley, 2006).
[CrossRef]

Yrjänäinen, J.

M. Trimeche, R. C. Bilcu, and J. Yrjänäinen, “Adaptive outlier rejection in image super-resolution”, EURASIP J. Appl. Signal Process. 2006, 38052 (2006).
[CrossRef]

Zhao, W.

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.

Zibetti, M. V. W.

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]

Zisserman, A.

A. Blake and A. Zisserman, Visual Reconstruction (MIT Press, 1987).

Zomet, A.

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]

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]

Appl. Opt. (4)

Comput. Vision Image Understand. (1)

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]

CVGIP Graph. Models Image Process. (2)

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” CVGIP Graph. Models Image Process. 54, 181-186 (1992).
[CrossRef]

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231-239(1991).
[CrossRef]

EURASIP J. Adv. Signal Process. (1)

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]

EURASIP J. Appl. Signal Process. (1)

M. Trimeche, R. C. Bilcu, and J. Yrjänäinen, “Adaptive outlier rejection in image super-resolution”, EURASIP J. Appl. Signal Process. 2006, 38052 (2006).
[CrossRef]

EURASIP J. Image Video Process. (1)

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]

IEEE Signal Process. Mag. (1)

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]

IEEE Trans. Acoust. Speech Signal Process. (1)

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).

IEEE Trans. Image Process. (7)

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]

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]

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573-583 (2001).
[CrossRef]

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]

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]

T. Rabie, “Robust estimation approach for blind denoising,” IEEE Trans. Image Process. 14, 1755-1765 (2005).
[CrossRef]

M. J. Black, G. Sapiro, D H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process. 7, 421-432 (1998).
[CrossRef]

Int. J. Comput. Vision (1)

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]

J. Phys. Conf. Ser. (1)

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.

Opt. Eng. (1)

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]

Opt. Express (1)

Proc. SPIE (1)

Z. A. Ivanovski, L. Panovski, and L. J. Karam, “Robust super-resolution based on pixel-level selectivity”, Proc. SPIE 6077, 607707 (2006).
[CrossRef]

Other (21)

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.

P. J. Huber, Robust Statistics, Wiley Series in Probability and Statistics (Wiley-Interscience, 2003).

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]

R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, Wiley Series in Probability and Statistics (Wiley, 2006).
[CrossRef]

N. Sebe and M. S. Lew, Robust Computer Vision: Theory and Applications (Springer, 2003).

D. P. Bertsekas, Nonlinear Programming (Athena Scientific, 1999).

A. Blake and A. Zisserman, Visual Reconstruction (MIT Press, 1987).

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.

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).

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.

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.

S. Chaudhuri, Super-Resolution Imaging (Norwell, 2001).

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.

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]

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]

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]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to super-resolution,” Proc. SPIE 5203,121-130 (2003).

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.”

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.

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).

D. Capel, Image Mosaicing and Super-resolution (Springer, 2004).
[CrossRef]

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Figures (6)

Fig. 1
Fig. 1

Sequence of operations of the imaging model in Eq. (1): (a) original HR image, (b) geometric warping, (c) blurring, (d) sampling, (e) noise addition. Middle column, reference SI; right column, SI with a relative rotation and offset with respect to the reference SI.

Fig. 2
Fig. 2

Plot of the error norms (left) and influence functions (right) for the L 2 , L 1 , and Lorentzian error norms. All the plots are normalized to illustrate the relative weight assigned to the errors.

Fig. 3
Fig. 3

Test sequence #1: a, original HR scene ( 600 × 800 ); b,  HR region of interest ( 256 × 256 ); c, LR image detected by the reference subimager, SI #1; d, LR image detected by SI #4, which has a relative offset and rotation with respect to SI #1; e, LR image detected by SI #10, which has a relative offset and magnification with respect to SI #1.

Fig. 4
Fig. 4

Plots of (a) average SAD values ( d k ), (b) outlier thresholds ( τ k ), (c) Lorentzian influence functions ( ψ k ): dash–dot, dot, and dashed curves correspond to SIs #4, #10, and #7, respectively, for test sequence #1.

Fig. 5
Fig. 5

4 × HR image reconstruction results for test sequence #1: a, original HR ROI; b, LR image detected by the reference SI, SI #1; c, initial estimate (bilinear interpolation of b); d,  L 2 estimate + Tikhonov regularization; e,  L 1 estimate + Tikhonov regularization; f, Lorentzian estimate [27] + Tikhonov regularization; g,  proposed algorithm without regularization; h, proposed algorithm + regularization;, i,  L 2 estimate + Tikhonov regularization, excluding SIs #4, #7, and #10 from the reconstruction process; j,  L 1 estimate + Tikhonov regularization, excluding SIs #4, #7, and #10 from the reconstruction process.

Fig. 6
Fig. 6

4 × HR image reconstruction results for test sequence #2: (a) LR frame #1, (b) LR frame #27, (c)  L 2 estimate + Tikhonov regularization, (d)  L 1 estimate + Tikhonov regularization, (e) Lorentzian estimate [27] + Tikhonov regularization, (f) proposed algorithm without regularization, (g) proposed algorithm + Tikhonov regularization, (h) plot of average SAD values ( d k ), (i) plot of the outlier thresholds ( τ k ), (j) plot of the Lorentzian influence functions ( ψ k ): dotted curves correspond to frames 26 through 30, which include head movement.

Equations (16)

Equations on this page are rendered with MathJax. Learn more.

Y k = D k H k F k X + N k , k = 1 , 2 , , L ,
Y k = D H F k X + N k , k = 1 , 2 , , L .
[ x y ] = s k [ cos θ k sin θ k sin θ k cos θ k ] [ x y ] + [ u k v k ] .
X * = arg min X k = 1 L ρ ( D H F k X Y k ) = arg min X k = 1 L ρ ( E k ) ,
X k = 1 L ρ ( E k ) = 0 k = 1 L ( D H F k ) T ψ ( E k ) = 0 ,
ρ ( e ; τ ) = log [ ( e 2 + τ 2 ) / τ 2 ] ,
ψ ( e ; τ ) = 2 τ e / ( e 2 + τ 2 ) .
X * = arg min X k = 1 L ρ ( D H F k X Y k ; τ k ) = arg min X k = 1 L ρ ( E k ; τ k ) ,
X n + 1 = X n η k = 1 L ρ ( E k n ; τ k ) , n = 0 , 1 , 2 , . . . ,
X n + 1 = X n η k = 1 L ( D H F k ) T ψ k n , n = 0 , 1 , 2 , . . . ,
X n + 1 = X n k = 1 L η k ( D H F k ) T ψ k n , n = 0 , 1 , 2 , . . . ,
η k = τ k / 2
τ k = τ 1 e α d k = 255 e α d k .
α = 1 d max log ( 255 τ min ) .
d k = 1 255 × M N x = 1 M y = 1 N | y 1 ( x , y ) y ˜ k ( x , y ) | .
τ k = 255 e 24 d k , k = 2 , 3 , , L ,

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