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Proceedings Paper

Depth-from-defocus: blur equalization technique
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Paper Abstract

A new spatial-domain Blur Equalization Technique (BET) is presented. BET is based on Depth-from-Defocus (DFD) technique. It relies on equalizing the blur or defocus of two different images recorded with different camera parameters. Also, BET facilitates modeling of images locally by higher order polynomials with lower series truncation errors. The accuracy of BET is further enhanced by discarding pixels with low Signal-to-Noise ratio by thresholding image Laplacians, and relying more on sharper of the two blurred images in estimating the blur parameters. BET is found to be superior to some of the best comparable DFD techniques in a large number of both simulation and actual experiments. Actual experiments used a large variety of objects including very low contrast digital camera test charts located at many different distances. In autofocusing experiments, BET gave an RMS error of 1.2% in lens position.

Paper Details

Date Published: 12 October 2006
PDF: 10 pages
Proc. SPIE 6382, Two- and Three-Dimensional Methods for Inspection and Metrology IV, 63820E (12 October 2006); doi: 10.1117/12.688615
Show Author Affiliations
Tao Xian, State Univ. of New York at Stony Brook (United States)
Murali Subbarao, State Univ. of New York at Stony Brook (United States)


Published in SPIE Proceedings Vol. 6382:
Two- and Three-Dimensional Methods for Inspection and Metrology IV
Peisen S. Huang, Editor(s)

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