Ronald Driggers, Editor-in-Chief
Jiazheng Shi, Stephen E. Reichenbach, and James D. Howe
Jiazheng Shi,1 Stephen E. Reichenbach,1 and James D. Howe2
1J. Shi (email@example.com)
and S. E. Reichenbach are with the Department of Computer Science and Engineering, University of Nebraska, Lincoln, Nebraska 68588-0115. USA
2J. D. Howe is with the Night Vision and Electronic Sensors Directorate, U.S. Army, Fort Belvoir, Virginia 22060. USA
Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented.
Microscanning creates multiple low-resolution images with slightly different sample–scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end,
continuous–discrete–continuous microscanning imaging system model.
Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.
© 2006 Optical Society of America
J. Ruiz-Alzola, C. Alberola-López, and C. F. Westin, "Adaptive kriging filters for multidimensional signal processing," Signal Process. 85, 413-439 (2005).
S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Advances and challenges in superresolution," Int. J. Imag. Syst. Technol. 14, 47-57 (2004).
S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Fast and robust multiframe super resolution," IEEE Trans. Image Process. 13, 13274-134 (2004).
E. Choi, J. Choi, and M. G. Kang, "Super-resolution approach to overcome physical limitations of imaging sensors: an overview," Int. J. Imag. Syst. Technol. 14, 36-46 (2004).
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).
M. K. Ng and N. K. Bose, "Mathematical analysis of super-resolution methodology," IEEE Signal Process. Mag. 20, 62-74 (2003).
M. G. Kang and S. Chaudhuri, "Super-resolution image reconstruction," IEEE Signal Process. Mag. 20(3), 19-20 (2003).
H. Foroosh, J. B. Zerubia, and B. Marc, "Extension of phase correlation to subpixel registration," IEEE Trans. Image Process. 11, 188-200 (2002).
M. Elad and Y. Hel-Or, "A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur," IEEE Trans. Image Process. 10, 1187-1193 (2001).
N. Nguyen and P. Milanfar, "A computationally efficient super-resolution image reconstruction algorithm," IEEE Trans. Image Process. 10, 5733-58 (2001).
T. Blu, P. Thévenaz, and M. Unser, "MOMS: maximal-order interpolation of minimal support," IEEE Trans. Image Process. 10, 1069-1080 (2001).
M. S. Alam, J. G. Bognar, R. C. Hardie, and B. J. Yasuda, "Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames," IEEE Trans. Instrum. Meas. 49, 915-923 (2000).
T. M. Lehmann, C. Gonner, and K. Spitzer, "Survey: interpolation methods in medical image processing," IEEE Trans. Med. Imag. 18, 1049-1075 (1999).
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).
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).
R. R. Schultz and R. L. Stevenson, "Extraction of high-resolution frames from video sequences," IEEE Trans. Image Process. 5, 996-1011 (1996).
S. P. Kim and W.-Y. Su, "Recursive high-resolution reconstruction of blurred multiframe images," IEEE Trans. Image Process. 2, 534-539 (1993).
S. E. Reichenbach and S. K. Park, "Small convolution kernels for high-fidelity image restoration," IEEE Trans. Signal Process. 39, 2263-2274 (1991).
M. Irani and S. Peleg, "Improving resolution by image registration," CVGIP Graph. Models Image Process. 53, 231-239 (1991).
S. E. Reichenbach, S. K. Park, and R. Narayanswamy, "Characterizing digital image acquisition devices," Opt. Eng. 30, 170-177 (1991).
S. P. Kim, N. K. Bose, and H. M. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Trans. Acoust. Speech Signal Process. 38, 1013-1027 (1990).
C. L. Fales, F. O. Huck, J. A. McCormick, and S. K. Park, "Wiener restoration of sampled image data: end-to-end analysis," J. Opt. Soc. Am. A 5, 300-314 (1988).
S. K. Park and R. A. Schowengerdt, "Image reconstruction by parametric cubic convolution," Comput. Vision Graph. Image Process. 23, 258-272 (1983).
R. G. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans. Acoust. Speech Signal Process. 29, 1153-1160 (1981).
E. H. Linfoot, "Transmission factors and optical design," J. Opt. Soc. Am. 46, 740-752 (1956).
R. L. Lagendijk and J. Biemond, "Basic methods for image restoration and identification," in Handbook of Image and Video Processing, A.Bovik, ed. (Academic, 2000).
C. L. L. Hendriks and L. J. V. Vliet, "Improving resolution to reduce aliasing in an undersampled image sequence," in Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications, M. M. Blouke, N. Sampat, G. M. Williams, and T. Yeh, eds., Proc. SPIE 3965, 1-9 (2000).
K. R. Castleman, Digital Image Processing (Prentice-Hall, 1979).
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 Press1984), pp. 317-339.
A. K. Katsaggelos, Digital Image Restoration (Springer-Verlag, 1991).
P. Milanfar, Resolution enhancement software, www.soe.ucsc.edu/∼milanfar/SR-software.htm (2004).
S. E. Reichenbach and J. Shi, "Two-dimensional cubic convolution for one-pass image restoration and reconstruction," in International Geoscience and Remote Sensing Symposium (Institute of Electrical and Electronics Engineers, 2004), pp. 2074-2076.
J. Shi and S. E. Reichenbach, "Image image interpolation by two-dimensional parametric cubic convolution," IEEE Trans. Image Process. (to be published).
R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 2nd ed. (Academic, 1997).
M. L. Stein, Interpolation of Spatial Data: Some Theory for Kriging (Springer-Verlag, 1999).
Nebraska Department of Natural Resources, Digital Orthophoto Quadrangle Database, www.dnr.state.ne.us/databank/coq.html (2004).
OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.
Alert me when this article is cited.
Click here to see a list of articles that cite this paper
End-to-end model of the digital imaging process.
Download Full Size | PPT Slide | PDF
Microscanning produces multiple images.
Small reconstruction and restoration kernels for the simulation experiment.
Low-resolution infrared image of a four-bar target used for estimating the acquisition transfer function.
Superresolution average scan of the bar target.
Estimated acquisition transfer function
Superresolution results for a microscanned infrared system.
Small reconstruction and restoration kernels for the real image experiment.
Table 1 Fidelity and Computational Costs of Various Methods
Equations on this page are rendered with MathJax. Learn more.