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Proceedings Paper

Shape recovery from a blurred image using wavelet analysis
Author(s): Bae Sung Kim; Joungil Yun; Tae-Sun Choi
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Paper Abstract

In this paper, a new method for obtaining 3D shape of an object by measuring relative blur between images using wavelet analysis has been described. Most of the previous methods use inverse filtering to determine the measure of defocus. These methods suffer from some fundamental problems like inaccuracies in finding the frequency domain representation, windowing effects, and border effects. Besides these deficiencies, a filter, such as Laplacian of Gaussian, that produces an aggregate estimate of defocus for an unknown texture, can not lead to accurate depth estimates because of the non-stationary nature of images. We propose a new depth from defocus (DFD) method using wavelet analysis that is capable of performing both the local analysis and the windowing technique with variable-sized regions for non- stationary images with complex textural properties. We show that normalized image ratio of wavelet power by Parseval's theorem is closely related to blur parameter and depth. Experimental results have been presented demonstrating that our DFD method is faster in speed and gives more precise shape estimates than previous DFD techniques for both synthetic and real scenes.

Paper Details

Date Published: 30 September 1999
PDF: 11 pages
Proc. SPIE 3815, Digital Image Recovery and Synthesis IV, (30 September 1999); doi: 10.1117/12.364135
Show Author Affiliations
Bae Sung Kim, Kwangju Institute of Science and Technology (South Korea)
Joungil Yun, Kwangju Institute of Science and Technology (South Korea)
Tae-Sun Choi, Kwangju Institute of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 3815:
Digital Image Recovery and Synthesis IV
Timothy J. Schulz; Paul S. Idell, Editor(s)

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