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 (firstname.lastname@example.org)
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
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End-to-end model of the digital imaging process.
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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
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