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Proceedings Paper

Designing the optimal convolution kernel for modeling the motion blur
Author(s): Jan Jelinek
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Paper Abstract

Motion blur acts on an image like a two dimensional low pass filter, whose spatial frequency characteristic depends both on the trajectory of the relative motion between the scene and the camera and on the velocity vector variation along it. When motion during exposure is permitted, the conventional, static notions of both the image exposure and the scene-toimage mapping become unsuitable and must be revised to accommodate the image formation dynamics. This paper develops an exact image formation model for arbitrary object-camera relative motion with arbitrary velocity profiles. Moreover, for any motion the camera may operate in either continuous or flutter shutter exposure mode. Its result is a convolution kernel, which is optimally designed for both the given motion and sensor array geometry, and hence permits the most accurate computational undoing of the blurring effects for the given camera required in forensic and high security applications. The theory has been implemented and a few examples are shown in the paper.

Paper Details

Date Published: 3 June 2011
PDF: 14 pages
Proc. SPIE 8056, Visual Information Processing XX, 80560A (3 June 2011); doi: 10.1117/12.887042
Show Author Affiliations
Jan Jelinek, Honeywell ACS Labs. (United States)

Published in SPIE Proceedings Vol. 8056:
Visual Information Processing XX
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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