Open AccessFeature visibility limits in the nonlinear enhancement of turbid images
Author(s) -
Daniel J. Jobson,
Zia-ur Rahman,
Glenn A. Woodell
Publication year - 2003
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.488842
Subject(s) - visibility , feature (linguistics) , artificial intelligence , computer vision , computer science , noise (video) , signal (programming language) , process (computing) , limit (mathematics) , feature detection (computer vision) , signal processing , pattern recognition (psychology) , image processing , image (mathematics) , optics , mathematics , physics , telecommunications , mathematical analysis , philosophy , linguistics , radar , programming language , operating system
The advancement of non-linear processing methods for generic automatic clarification of turbid imagery has led us from extensions of entirely passive multiscale Retinex processing to a new framework of active measurement and control of the enhancement process called the Visual Servo. In the process of testing this new non-linear computational scheme, we have identified that feature visibility limits in the post-enhancement image now simplify to a single signal-to-noise figure of merit: a feature is visible if the feature-background signal dierence is greater than the RMS noise level. In other words, a signal-to-noise limit of approximately unity constitutes a lower limit on feature visibility.
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