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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 29, Iss. 8 — Aug. 1, 2012
  • pp: 1580–1587

Nature-inspired color-filter array for enhancing the quality of images

Julien Couillaud, Alain Horé, and Djemel Ziou  »View Author Affiliations


JOSA A, Vol. 29, Issue 8, pp. 1580-1587 (2012)
http://dx.doi.org/10.1364/JOSAA.29.001580


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Abstract

In this paper, we study the rendering of images with a new mosaic/color-filter array (CFA) called the Burtoni mosaic. This mosaic is derived from the retina of the African cichlid fish Astatotilapia burtoni. To evaluate the effect of the Burtoni mosaic on the quality of the rendered images, we use two quality measures in the Fourier domain, which are the resolution error and the aliasing error. Conversely to many approaches that use demosaicing algorithms to assess the quality of the reconstruction of images by a CFA, no demosaicing algorithm is used in our model, which makes it independent of such algorithms. We also use 11 semantic sets of color images in order to highlight the image classes that are well fitted for the Burtoni mosaic in the process of image acquisition. We have compared the Burtoni mosaic with the Bayer CFA and with an optimal CFA proposed by Hao et al. Experiments have shown that the Burtoni mosaic gives the best performances for images of nine semantic sets, which are the high frequency, aerial, indoor, face, aquatic, bright, dark, step, and line classes.

© 2012 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.2980) Image processing : Image enhancement
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

History
Original Manuscript: April 5, 2012
Revised Manuscript: June 19, 2012
Manuscript Accepted: June 21, 2012
Published: July 19, 2012

Virtual Issues
Vol. 7, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Julien Couillaud, Alain Horé, and Djemel Ziou, "Nature-inspired color-filter array for enhancing the quality of images," J. Opt. Soc. Am. A 29, 1580-1587 (2012)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-29-8-1580


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References

  1. P. Hao, Y. Li, Z. Lin, and E. Dubois, “A geometric method for optimal design of color filter arrays,” IEEE Trans. Image Process. 20, 709–722 (2011). [CrossRef]
  2. B. E. Bayer, “Color imaging array,” U.S. patent 3971065 (1976).
  3. R. Mersereau, “The processing of hexagonally sampled two-dimensional signals,” Proc. IEEE 67, 930–949 (1979). [CrossRef]
  4. R. Ramanath, W. E. Snyder, G. L. Bilbro, and W. A. Sander, “Demosaicking methods for Bayer color arrays,” J. Electron. Imaging 11, 306–315 (2002). [CrossRef]
  5. R. Lukac and K. N. Plataniotis, “Color filter arrays: design and performance analysis,” IEEE Trans. Consum. Electron. 51, 1260–1267 (2005). [CrossRef]
  6. C. Elliot, “Reducing pixel count without reducing image quality,” Inf. Disp. 15, 22–25 (1999).
  7. C. Elliot, T. Credelle, S. Han, M. Im, M. Higgins, and P. Higgins, “Development of the pentile matrix color amlcd subpixel architecture and rendering algorithms,” J. Soc. Inf. Disp. 11, 89–98 (2003). [CrossRef]
  8. M. A. Klompenhouwer and G. Haan, “Subpixel image scaling for color-matrix displays,” J. Soc. Inf. Disp. 11, 99–108 (2003). [CrossRef]
  9. H. Hirakawa and P. J. Wolfe, “Spatio-spectral color filter array design for optimal image recovery,” IEEE Trans. Image Process. 17, 1876–1890 (2008). [CrossRef]
  10. R. Kro¨ger, “Anti-aliasing in image recording and display hardware: lessons from nature,” J. Opt. A 6, 743–748 (2004). [CrossRef]
  11. R. D. Fernald and P. A. Liebman, “Visual receptor pigments in the african cichlid fish haplochromis burtoni,” Vis. Res. 20, 857–864 (1980). [CrossRef]
  12. N. V. van Cichlidenliefhebbers, “Astatotilapia burtoni,” http://www.nvcweb.nl (available online, June2012).
  13. G. C. Holst, Sampling, Aliasing and Data Fidelity (JCD Publishing, 1998).
  14. I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” J. Electron. Imaging 11, 206–223 (2002). [CrossRef]
  15. R. Kreis, “Issues of spectral quality in clinical h-magnetic resonance spectroscopy and a gallery of artifacts,” NMR Biomed. 17, 361–381 (2004). [CrossRef]
  16. J. E. Farrell, “Image quality evaluation,” in Colour Imaging: Vision and Technology, L. W. MacDonald and M. R. Luo, eds. (Wiley, 1999).
  17. M. Miyahara, K. Kotani, and V. R. Algazi, “Objective picture quality scale for image coding,” IEEE Trans. Commun. 46, 1215–1226 (1998). [CrossRef]
  18. M. Cadik and P. Slavik, “Evaluation of two principal approaches to objective image quality assessment,” in International Conference on Information Visualisation (IEEE, 2004), pp. 513–551.
  19. E. Miguel, B. Zanoguera, J. R. Castillo, and M. Vera-Perez, “Use of the modulation transfer function to measure quality of digital cameras,” in IEEE International Conference on Electronics (IEEE, 2006), pp. 52–56.
  20. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004). [CrossRef]
  21. A. Horé and D. Ziou, “Image quality metrics: psnr vs. ssim,” in International Conference on Pattern Recognition (IEEE Computer Society, 2010), pp. 2366–2369.
  22. E. Ong, W. Lin, Z. Lu, X. Yand, S. Yao, F. Pan, L. Jiang, and F. Moschetti, “A no-reference quality metric for measuring image blur,” in International Symposium on Signal Processing and its Applications (IEEE, 2003), pp. 469–472.
  23. R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007). [CrossRef]
  24. A. Horé and D. Ziou, “An edge-sensing generic demosaicing algorithm with application to image resampling,” IEEE Trans. Image Process. 20, 3136–3150 (2011). [CrossRef]
  25. U. Barnhöfer, J. M. Dicarlo, B. Olding, and B. A. Wandell, “Color estimation error trade-offs,” in SPIE-IS&T Electronic Imaging (2003), pp. 263–273.
  26. G. Kutas, H. K. Choh, Y. Kwak, P. Bodrogi, and L. Czuni, “Subpixel arrangement and color image rendering methods for multiprimary displays,” J. Electron. Imaging 15, 023002 (2006). [CrossRef]
  27. L. Condat, “A new random color filter array with good spectral properties,” in International Conference on Image Processing (IEEE Computer Society, 2009), pp. 1593–1596.
  28. L. Condat, “Color filter array design using random patterns with blue noise chromatic spectra,” Image Vis. Comput. 28, 1196–1202 (2010). [CrossRef]
  29. A. Papoulis, “Generalized sampling theorem,” IEEE Trans. Circuit Syst. 24, 652–654 (1977). [CrossRef]
  30. A. Horé, D. Ziou, and F. Deschênes, “A new image scaling algorithm based on the sampling theorem of papoulis and application to color,” in International Conference on Image and Graphics (IEEE, 2007), pp. 39–44.
  31. F. Deschênes, D. Ziou, and P. Fuchs, “An unified approach for a simultaneous and cooperative estimation of defocus blur and spatial shifts,” Image Vis. Comput. 22, 35–57 (2004). [CrossRef]

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