
Proceedings Paper
Efficient super-resolution image reconstruction applied to surveillance video captured by small unmanned aircraft systemsFormat | Member Price | Non-Member Price |
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Paper Abstract
The concept surrounding super-resolution image reconstruction is to recover a highly-resolved
image from a series of low-resolution images via between-frame subpixel image
registration. In this paper, we propose a novel and efficient super-resolution algorithm, and then
apply it to the reconstruction of real video data captured by a small Unmanned Aircraft System
(UAS). Small UAS aircraft generally have a wingspan of less than four meters, so that these vehicles
and their payloads can be buffeted by even light winds, resulting in potentially unstable video. This
algorithm is based on a coarse-to-fine strategy, in which a coarsely super-resolved image sequence is
first built from the original video data by image registration and bi-cubic interpolation between a
fixed reference frame and every additional frame. It is well known that the median filter is robust to
outliers. If we calculate pixel-wise medians in the coarsely super-resolved image sequence, we can
restore a refined super-resolved image. The primary advantage is that this is a noniterative algorithm,
unlike traditional approaches based on highly-computational iterative algorithms. Experimental
results show that our coarse-to-fine super-resolution algorithm is not only robust, but also very
efficient. In comparison with five well-known super-resolution algorithms, namely the robust super-resolution
algorithm, bi-cubic interpolation, projection onto convex sets (POCS), the Papoulis-Gerchberg algorithm, and the iterated back projection algorithm, our proposed algorithm gives both
strong efficiency and robustness, as well as good visual performance. This is particularly useful for
the application of super-resolution to UAS surveillance video, where real-time processing is highly
desired.
Paper Details
Date Published: 17 April 2008
PDF: 11 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 696814 (17 April 2008); doi: 10.1117/12.767826
Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)
PDF: 11 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 696814 (17 April 2008); doi: 10.1117/12.767826
Show Author Affiliations
Qiang He, Mississippi Valley State Univ. (United States)
Richard R. Schultz, Univ. of North Dakota (United States)
Richard R. Schultz, Univ. of North Dakota (United States)
Chee-Hung Henry Chu, Univ. of Louisiana at Lafayette (United States)
Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)
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