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Proceedings Paper

Autofocus survey: a comparison of algorithms
Author(s): Loren Shih
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Paper Abstract

There are numerous passive contrast sensing autofocus algorithms that are well documented in literature, but some aspects of their comparative performance have not been widely researched. This study explores the relative merits of a set of autofocus algorithms via examining them against a variety of scene conditions. We create a statistics engine that considers a scene taken through a range of focal values and then computes the best focal position using each autofocus algorithm. The process is repeated across a survey of test scenes containing different representative conditions. The results are assessed against focal positions which are determined by manually focusing the scenes. Through examining these results, we then derive conclusions about the relative merits of each autofocus algorithm with respect to the criteria accuracy and unimodality. Our study concludes that the basic 2D spatial gradient measurement approaches yield the best autofocus results in terms of accuracy and unimodality.

Paper Details

Date Published: 20 February 2007
PDF: 11 pages
Proc. SPIE 6502, Digital Photography III, 65020B (20 February 2007); doi: 10.1117/12.705386
Show Author Affiliations
Loren Shih, Cypress Semiconductor (United States)


Published in SPIE Proceedings Vol. 6502:
Digital Photography III
Russel A. Martin; Jeffrey M. DiCarlo; Nitin Sampat, Editor(s)

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