Ronald Driggers, Editor-in-Chief
Mohammad S. Alam, John G. Bognar, Steve Cain, and Brian J. Yasuda
Mohammad S. Alam, John G. Bognar, Steve Cain, and Brian J. Yasuda
M. S. Alam is with the Department of Electrical Engineering, Purdue University, Fort Wayne, Indiana 46805-1499 and the Graduate Faculty, Indiana University, Bloomington, Indiana 47404.
J. G. Bognar is with Technology/Scientific Services, Inc., P.O. Box 3065, Dayton, Ohio 45437.
S. Cain and B. J. Yasuda are with Wright Laboratory, Sensor Technology Branch, WL/AAJT, Building 622, 3109 P Street, Wright-Patterson Air Force Base, Dayton, Ohio 45433.
During the process of microscanning a controlled vibrating mirror
typically is used to produce subpixel shifts in a sequence of
forward-looking infrared (FLIR) images. If the FLIR is mounted
on a moving platform, such as an aircraft, uncontrolled random
vibrations associated with the platform can be used to generate the
shifts. Iterative techniques such as the expectation-maximization
(EM) approach by means of the maximum-likelihood algorithm can be
used to generate high-resolution images from multiple randomly shifted
aliased frames. In the maximum-likelihood approach the data are
considered to be Poisson random variables and an EM algorithm is
developed that iteratively estimates an unaliased image that is
compensated for known imager-system blur while it simultaneously
estimates the translational shifts. Although this algorithm yields
high-resolution images from a sequence of randomly shifted frames, it
requires significant computation time and cannot be implemented for
real-time applications that use the currently available
high-performance processors. The new image shifts are iteratively
calculated by evaluation of a cost function that compares the shifted
and interlaced data frames with the corresponding values in the
algorithm’s latest estimate of the high-resolution image. We
present a registration algorithm that estimates the shifts in one
step. The shift parameters provided by the new algorithm are
accurate enough to eliminate the need for iterative recalculation of
translational shifts. Using this shift information, we apply a
simplified version of the EM algorithm to estimate a high-resolution
image from a given sequence of video frames. The proposed modified
EM algorithm has been found to reduce significantly the computational
burden when compared with the original EM algorithm, thus making it
more attractive for practical implementation. Both simulation and
experimental results are presented to verify the effectiveness of the
© 1998 Optical Society of America
R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1628 (1997).
Y. You, M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Process. 5, 416–428 (1996).
R. R. Schulz, R. L. Stevenson, “Extraction of high resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996–1002 (1996).
J. C. Gillette, T. M. Stadmiller, R. C. Hardie, “Reduction of aliasing in staring infrared imagers utilizing subpixel techniques,” Opt. Eng. 34, 3130–3137 (1995).
R. R. Schulz, R. L. Stevenson, “A Bayesian approach to image expansion for improved definition,” IEEE Trans. Image Process. 3, 233–242 (1994).
T. J. Schulz, “Multiframe blind deconvolution of astronomical images,” J. Opt. Soc. Am. A 10, 1064–1069 (1993).
M. Irani, S. Peleg, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process. 53, 231–239 (1991).
L. Shepp, Y. Vardi, “Maximum likelihood reconstruction in positron emission tomography,” IEEE Trans. Med. Imag. M1-1, 113–122 (1982).
M. S. Alam, J. G. Bognar, B. J. Yasuda, R. C. Hardie, “High-resolution infrared image reconstruction using multiple randomly shifted, low-resolution, aliased frames,” in Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VIII, G. C. Holst, ed., Proc. SPIE3063, 102–112 (1997).
M. S. Alam, “Fast infrared image registration and high-resolution reconstruction for real time applications,” in 1996 Annual Research Report (Air Force Office of Scientific Research, Bolling Air Force Base, Washington, D.C.) (in press).
E. Armstrong, J. Bognar, B. Yasuda, R. Hardie, “The application of Wiener filters to microscan imaging,” in Proceedings of the 1996 IRIS Specialty Group Meeting on Passive Sensors, E. Williams, ed. (Infrared Information Analysis Center, Ann Arbor, Mich., 1996), Vol. 1, pp. 359–375.
E. A. Watson, R. A. Muse, F. P. Blommel, “Aliasing and blurring in microscanned imagery,” in Infrared Imaging Systems: Design, Analysis, Modeling, and Testing III, G. C. Holst, ed., Proc. SPIE1689, 242–250 (1992).
S. Cain, R. C. Hardie, “Restoration of aliased video sequences via a maximum-likelihood approach,” in Proceedings of the 1996 IRIS Specialty Group Meeting on Passive Sensors, E. Williams, ed. (Infrared Information Analysis Center, Ann Arbor, Mich., 1996), Vol. 1, pp. 377–390.
Y. P. Hung, D. B. Cooper, “Maximum a posteriori probability 3D surface reconstruction using multiple intensity images directly,” in Sensing and Reconstruction of Three-Dimensional Objects and Scenes, B. Girod, ed., Proc. SPIE1260, 36–48 (1990).
R. C. Gonzalez, R. E. Woods, Digital Image Processing (Addison-Wesley, Reading, Mass., 1992).
J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1996).
E. Kaltenbacher, R. C. Hardie, “Infrared image registration and high-resolution reconstruction,” in Proceedings of the National Aeronautics and Electronics Conference (Institute of Electrical and Electronics Engineers, New York, 1996), pp. 702–709.
A. Schaum, M. McHugh, “Analytic methods of image registration: displacement estimation and resampling,” NRL Rep. 9298 (Naval Research Laboratory, Washington, D.C., 28February1991).
W. K. Pratt, Digital Image Processing (Wiley, New York, 1992).
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
(a) High-resolution image. (b) Low-resolution
Download Full Size | PPT Slide | PDF
(a) Original input scene. (b) Staring frame
sampled by the simulated detector array.
Theoretical (X) and estimated
(O) registration parameters for 16 simulated frames.
Simulated FLIR Images: (a) High-resolution frame
reconstructed by use of the original EM approach. (b)
High-resolution frame reconstructed by use of the modified EM
Real FLIR Images: (a) Staring frame. (b)
Staring frame after bicubic interpolation. (c) High-resolution
image reconstructed from 16 real FLIR data frames by use of the
original EM algorithm. (d) High-resolution image reconstructed
from 16 real FLIR data frames by use of the modified EM algorithm.
Table 1 Comparison of Processing Speeds of the Original EM
Algorithm with the Modified EM Algorithm
Equations on this page are rendered with MathJax. Learn more.