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Optics Express

Optics Express

  • Editor: Michael Duncan
  • Vol. 11, Iss. 18 — Sep. 8, 2003
  • pp: 2118–2125

Multispectral principal component imaging

Himadri S. Pal and Mark A. Neifeld  »View Author Affiliations

Optics Express, Vol. 11, Issue 18, pp. 2118-2125 (2003)

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We analyze a novel multispectral imager that directly measures the principal component features of an object. Optical feature extraction is studied for color face images, multi-spectral LANDSAT-7 images, and their grayscale equivalents. Blockwise feature extraction is performed that exploits both spatial and spectral correlation, with the goal of enhancing feature fidelity (i.e., root mean square error). The effect of varying block size, number of features, and detector noise is studied in order to quantify feature fidelity and optimize reconstruction performance. These results are compared with conventional imaging and demonstrate the advantages of the multiplexed approach. Specifically, we find that in addition to reducing the number of detectors within the imager, the reconstruction fidelity (i.e., root mean square error) can be significantly improved using a feature-specific imager.

© 2003 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.5010) Image processing : Pattern recognition
(110.0110) Imaging systems : Imaging systems
(200.4740) Optics in computing : Optical processing

ToC Category:
Focus Issue: Integrated computational imaging systems

Original Manuscript: July 9, 2003
Revised Manuscript: August 14, 2003
Published: September 8, 2003

Himadri Pal and Mark Neifeld, "Multispectral principal component imaging," Opt. Express 11, 2118-2125 (2003)

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