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Depth-from-defocus: Blur equalization technique

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

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.

Original languageEnglish
Title of host publicationTwo- and Three-Dimensional Methods for Inspection and Metrology IV
DOIs
StatePublished - 2006
EventTwo- and Three-Dimensional Methods for Inspection and Metrology IV - Boston, MA, United States
Duration: Oct 1 2006Oct 3 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6382
ISSN (Print)0277-786X

Conference

ConferenceTwo- and Three-Dimensional Methods for Inspection and Metrology IV
Country/TerritoryUnited States
CityBoston, MA
Period10/1/0610/3/06

Keywords

  • Blur equalization technique
  • Depth-from-defocusing
  • Spatial-domain

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