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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 7 — Aug. 1, 2013

Acquisition and visualization techniques for narrow spectral color imaging

László Neumann, Rafael García, János Basa, and Ramón Hegedüs  »View Author Affiliations


JOSA A, Vol. 30, Issue 6, pp. 1039-1052 (2013)
http://dx.doi.org/10.1364/JOSAA.30.001039


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Abstract

This paper introduces a new approach in narrow-band imaging (NBI). Existing NBI techniques generate images by selecting discrete bands over the full visible spectrum or an even wider spectral range. In contrast, here we perform the sampling with filters covering a tight spectral window. This image acquisition method, named narrow spectral imaging, can be particularly useful when optical information is only available within a narrow spectral window, such as in the case of deep-water transmittance, which constitutes the principal motivation of this work. In this study we demonstrate the potential of the proposed photographic technique on nonunderwater scenes recorded under controlled conditions. To this end three multilayer narrow bandpass filters were employed, which transmit at 440, 456, and 470 nm bluish wavelengths, respectively. Since the differences among the images captured in such a narrow spectral window can be extremely small, both image acquisition and visualization require a novel approach. First, high-bit-depth images were acquired with multilayer narrow-band filters either placed in front of the illumination or mounted on the camera lens. Second, a color-mapping method is proposed, using which the input data can be transformed onto the entire display color gamut with a continuous and perceptually nearly uniform mapping, while ensuring optimally high information content for human perception.

© 2013 Optical Society of America

OCIS Codes
(330.1720) Vision, color, and visual optics : Color vision
(330.6180) Vision, color, and visual optics : Spectral discrimination
(110.4234) Imaging systems : Multispectral and hyperspectral imaging
(010.7295) Atmospheric and oceanic optics : Visibility and imaging

ToC Category:
Imaging Systems

History
Original Manuscript: November 20, 2012
Revised Manuscript: March 24, 2013
Manuscript Accepted: March 24, 2013
Published: May 6, 2013

Virtual Issues
Vol. 8, Iss. 7 Virtual Journal for Biomedical Optics

Citation
László Neumann, Rafael García, János Basa, and Ramón Hegedüs, "Acquisition and visualization techniques for narrow spectral color imaging," J. Opt. Soc. Am. A 30, 1039-1052 (2013)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-30-6-1039


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References

  1. K. Kuznetsov, R. Lambert, and J.-F. Rey, “Narrow-band imaging: potential and limitations,” Endoscopy 38, 76–81 (2006). [CrossRef]
  2. J. F. Bell, R. B. Anderson, K. Kressler, M. J. Wolff, and B. Cantor, “Color mosaics and multispectral analyses of Mars reconnaissance orbit mars color imager (MARCI) observations,” in Proceedings of American Geophysical Union (AGU) Fall Meeting Abstracts (2008), paper P32B-08.
  3. S. Moser, T. Müller, M.-O. Ebert, S. Jockusch, N. J. Turro, and B. Kräutler, “Blue luminescence of ripening bananas,” Angew. Chem. Int. Ed. Engl., Suppl. 47, 8954–8957 (2008). [CrossRef]
  4. P. M. Mehl, Y.-R. Chen, M. S. Kim, and D. E. Chan, “Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations,” J. Food Eng. 61, 67–81 (2004). [CrossRef]
  5. B. Wozniak and J. Dera, Light Absorption in Sea Water (Springer, 2007).
  6. J. D. Rancourt, Optical Thin Films: User Handbook (SPIE, 1996).
  7. Andover Catalog, Quality Optical Filters and Coatings, Catalog rev: Q-295, Andover Corporation, www.andovercorp.com (2011), p. 4.
  8. P. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in ACM Computer Graphics, Proceedings of SIGGRAPH (ACM, 1997), pp. 369–378.
  9. J. S. Tyo, A. Konsolakis, D. I. Diersen, and R. C. Olsen, “Principal-components-based display strategy for spectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 708–718 (2003). [CrossRef]
  10. J. Wang and I. C. Chein, “Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis,” IEEE Trans. Geosci. Remote Sens. 44, 1586–1600 (2006). [CrossRef]
  11. Y. Zhu, P. K. Varshney, and H. Chen, “Evaluation of ICA based fusion of hyperspectral images for color display,” in Proceedings of 10th International Conference on Information Fusion (IEEE, 2007), pp. 1–7.
  12. M. Cui, A. Razdan, J. Hu, and P. Wonka, “Interactive hyperspectral image visualization using convex optimization,” IEEE Trans. Geosci. Remote Sens. 47, 1673–1684 (2009). [CrossRef]
  13. Z. Mahmood and P. Scheunders, “Enhanced visualization of hyperspectral images,” IEEE Geosci. Remote Sens. Lett. 8, 869–873 (2011). [CrossRef]
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2006).
  15. L. Neumann and A. Neumann, “Color style transfer techniques using hue, lightness and saturation histogram matching,” in First Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging (Springer-Verlag, 2005), pp. 111–122.
  16. J. Morovic, J. Shaw, and P. Sun, “A fast, non-iterative, and exact histogram matching algorithm,” Pattern Recogn. Lett. 23, 127–135 (2002). [CrossRef]
  17. D. L. Ruderman, T. W. Cronin, and C. C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” J. Opt. Soc. Am. A 15, 2036–2045 (1998). [CrossRef]
  18. M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31, 320–330 (2006). [CrossRef]
  19. R. W. G. Hunt, Measuring Colour, 3rd ed. (Fountain, 2001).
  20. Brain Facts: A Primer on the Brain and Nervous System, 7th ed. (Society for Neuroscience, 2012).
  21. K. Matkovic, A. Neumann, L. Neumann, T. Psik, and W. Purgathofer, “Global contrast factor—a new approach to image contrast,” in First Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging, L. Neumann, M. Sbert, B. Gooch, and W. Purgathofer, eds. (Eurographics Book Series, 2005), pp. 159–167.
  22. T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. (Wiley-Interscience, 2006).
  23. J. Rigau, M. Feixas, and M. Sbert, “Image information in digital photography,” Lect. Notes Comput. Sci. 6469, 122–131 (2010). [CrossRef]
  24. R. W. Yeung, Information Theory and Network Coding (Springer, 2008).
  25. W. J. McGill, “Multivariate information transmission,” Psychometrika 19, 97–116 (1954).
  26. A. J. Bell, “The co-information lattice,” in Proceedings of 4th International Symposium on Independent Component Analysis and Blind Source Separation (Springer-Verlag, 2003), pp. 921–926.
  27. S. Watanabe, “Information theoretical analysis of multivariate correlation,” IBM J. Res. Dev. 4, 66–82 (1960). [CrossRef]
  28. T. V. van de Cruys, “Two multivariate generalizations of pointwise mutual information,” presented at DiSCO 2011: Workshop on Distributional Semantics and Compositionality (DiSCo at ACL-HLT 2011), Portland, Oregon, June 24, 2011.
  29. V. Tsagaris and V. Anastassopoulos, “Global measure for assessing image fusion methods,” Opt. Eng. 45, 026201 (2006). [CrossRef]
  30. D. Pál, B. Poczos, and C. Szepesvari, “Estimation of Renyi entropy and mutual information based on generalized nearest-neighbor graphs,” Adv. Neural Inform. Process. Syst. 23, 1849–1857 (2010).
  31. W. Li, “Mutual information functions versus correlation functions,” J. Stat. Phys. 60, 823–837 (1990). [CrossRef]
  32. K. Hoshino, F. Nielsen, and T. Nishimura, “Noise reduction in CMOS image sensors for high quality imaging: the autocorrelation function filter on burst image sequences,” ICGST-GVIP J. 7(3), 17–24 (2007).

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