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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 45, Iss. 28 — Oct. 1, 2006
  • pp: 7382–7391

Feature-specific structured imaging

Pawan K. Baheti and Mark A. Neifeld  »View Author Affiliations

Applied Optics, Vol. 45, Issue 28, pp. 7382-7391 (2006)

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We present a feature-specific imaging system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all the reflected light onto a single photodetector. Principal component features are used to define the illumination patterns. The optimal linear minimum mean-square error (LMMSE) operator is used to generate object estimates from the measured features. We study the optimal allocation of illumination energy into each feature measurement in the presence of additive white Gaussian detector noise and optical blur. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. Compared to the optimal LMMSE postprocessing of a conventional image, feature-specific structured imaging provides a 38 % rms error reduction and requires 400 times fewer measurements for a noise standard deviation of σ = 2 × 10 - 3 . Experimental results validate these theoretical predictions.

© 2006 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(110.0110) Imaging systems : Imaging systems
(150.2950) Machine vision : Illumination

ToC Category:
Imaging Systems

Original Manuscript: March 24, 2006
Revised Manuscript: May 8, 2006
Manuscript Accepted: May 19, 2006

Pawan K. Baheti and Mark A. Neifeld, "Feature-specific structured imaging," Appl. Opt. 45, 7382-7391 (2006)

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