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Proceedings Paper

Multiframe combination and blur deconvolution of video data
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Paper Abstract

In this paper we present a technique that may be applied to surveillance video data to obtain a higher-quality image from a sequence of lower-quality images. The increase in quality is derived through a deconvolution of optical blur and/or an increase in spatial sampling. To process sequences of real forensic video data, three main steps are required: frame and region selection, displacement estimation, and original image estimation. A user-identified region-of-interest (ROI) is compared to other frames in the sequence. The areas that are suitable matches are identified and used for displacement estimation. The calculated displacement vector images describe the transformation of the desired high-quality image to the observed low quality images. The final stage is based on the Projection Onto Convex Sets (POCS) super-resolution approach of Patti, Sezan, and Tekalp. This stage performs a deconvolution using the observed image sequence, displacement vectors, and an a priori known blur model. A description of the algorithmic steps are provided, and an example input sequence with corresponding output image is given.

Paper Details

Date Published: 19 April 2000
PDF: 8 pages
Proc. SPIE 3974, Image and Video Communications and Processing 2000, (19 April 2000); doi: 10.1117/12.383016
Show Author Affiliations
Timothy F. Gee, Oak Ridge National Lab. (United States)
Thomas P. Karnowski, Oak Ridge National Lab. (United States)
Kenneth W. Tobin, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 3974:
Image and Video Communications and Processing 2000
Bhaskaran Vasudev; T. Russell Hsing; Andrew G. Tescher; Robert L. Stevenson, Editor(s)

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