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

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  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).
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    [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]
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    [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]

2011 (2)

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]

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]

2010 (1)

L. Condat, “Color filter array design using random patterns with blue noise chromatic spectra,” Image Vis. Comput. 28, 1196–1202 (2010).
[CrossRef]

2008 (1)

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]

2007 (1)

R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007).
[CrossRef]

2006 (1)

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]

2005 (1)

R. Lukac and K. N. Plataniotis, “Color filter arrays: design and performance analysis,” IEEE Trans. Consum. Electron. 51, 1260–1267 (2005).
[CrossRef]

2004 (4)

R. Kro¨ger, “Anti-aliasing in image recording and display hardware: lessons from nature,” J. Opt. A 6, 743–748 (2004).
[CrossRef]

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]

R. Kreis, “Issues of spectral quality in clinical h-magnetic resonance spectroscopy and a gallery of artifacts,” NMR Biomed. 17, 361–381 (2004).
[CrossRef]

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]

2003 (2)

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]

M. A. Klompenhouwer and G. Haan, “Subpixel image scaling for color-matrix displays,” J. Soc. Inf. Disp. 11, 99–108 (2003).
[CrossRef]

2002 (2)

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]

I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” J. Electron. Imaging 11, 206–223 (2002).
[CrossRef]

1999 (1)

C. Elliot, “Reducing pixel count without reducing image quality,” Inf. Disp. 15, 22–25 (1999).

1998 (1)

M. Miyahara, K. Kotani, and V. R. Algazi, “Objective picture quality scale for image coding,” IEEE Trans. Commun. 46, 1215–1226 (1998).
[CrossRef]

1980 (1)

R. D. Fernald and P. A. Liebman, “Visual receptor pigments in the african cichlid fish haplochromis burtoni,” Vis. Res. 20, 857–864 (1980).
[CrossRef]

1979 (1)

R. Mersereau, “The processing of hexagonally sampled two-dimensional signals,” Proc. IEEE 67, 930–949 (1979).
[CrossRef]

1977 (1)

A. Papoulis, “Generalized sampling theorem,” IEEE Trans. Circuit Syst. 24, 652–654 (1977).
[CrossRef]

Algazi, V. R.

M. Miyahara, K. Kotani, and V. R. Algazi, “Objective picture quality scale for image coding,” IEEE Trans. Commun. 46, 1215–1226 (1998).
[CrossRef]

Avcibas, I.

I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” J. Electron. Imaging 11, 206–223 (2002).
[CrossRef]

Barnhöfer, U.

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.

Bayer, B. E.

B. E. Bayer, “Color imaging array,” U.S. patent 3971065 (1976).

Bilbro, G. L.

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]

Bodrogi, P.

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]

Bovik, A. C.

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]

Cadik, M.

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.

Castillo, J. R.

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.

Choh, H. K.

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]

Condat, L.

L. Condat, “Color filter array design using random patterns with blue noise chromatic spectra,” Image Vis. Comput. 28, 1196–1202 (2010).
[CrossRef]

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.

Credelle, T.

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]

Czuni, L.

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]

Deschênes, F.

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]

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.

Dicarlo, J. M.

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.

Dubois, E.

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]

Elliot, C.

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]

C. Elliot, “Reducing pixel count without reducing image quality,” Inf. Disp. 15, 22–25 (1999).

Farrell, J. E.

J. E. Farrell, “Image quality evaluation,” in Colour Imaging: Vision and Technology, L. W. MacDonald and M. R. Luo, eds. (Wiley, 1999).

Fernald, R. D.

R. D. Fernald and P. A. Liebman, “Visual receptor pigments in the african cichlid fish haplochromis burtoni,” Vis. Res. 20, 857–864 (1980).
[CrossRef]

Fuchs, P.

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]

Haan, G.

M. A. Klompenhouwer and G. Haan, “Subpixel image scaling for color-matrix displays,” J. Soc. Inf. Disp. 11, 99–108 (2003).
[CrossRef]

Hain, R.

R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007).
[CrossRef]

Han, S.

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]

Hao, P.

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]

Higgins, M.

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]

Higgins, P.

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]

Hirakawa, H.

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]

Holst, G. C.

G. C. Holst, Sampling, Aliasing and Data Fidelity (JCD Publishing, 1998).

Horé, A.

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]

A. Horé and D. Ziou, “Image quality metrics: psnr vs. ssim,” in International Conference on Pattern Recognition (IEEE Computer Society, 2010), pp. 2366–2369.

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.

Im, M.

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]

Jiang, L.

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.

Kahler, C. J.

R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007).
[CrossRef]

Klompenhouwer, M. A.

M. A. Klompenhouwer and G. Haan, “Subpixel image scaling for color-matrix displays,” J. Soc. Inf. Disp. 11, 99–108 (2003).
[CrossRef]

Kotani, K.

M. Miyahara, K. Kotani, and V. R. Algazi, “Objective picture quality scale for image coding,” IEEE Trans. Commun. 46, 1215–1226 (1998).
[CrossRef]

Kreis, R.

R. Kreis, “Issues of spectral quality in clinical h-magnetic resonance spectroscopy and a gallery of artifacts,” NMR Biomed. 17, 361–381 (2004).
[CrossRef]

Kro¨ger, R.

R. Kro¨ger, “Anti-aliasing in image recording and display hardware: lessons from nature,” J. Opt. A 6, 743–748 (2004).
[CrossRef]

Kutas, G.

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]

Kwak, Y.

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]

Li, Y.

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]

Liebman, P. A.

R. D. Fernald and P. A. Liebman, “Visual receptor pigments in the african cichlid fish haplochromis burtoni,” Vis. Res. 20, 857–864 (1980).
[CrossRef]

Lin, W.

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.

Lin, Z.

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]

Lu, Z.

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.

Lukac, R.

R. Lukac and K. N. Plataniotis, “Color filter arrays: design and performance analysis,” IEEE Trans. Consum. Electron. 51, 1260–1267 (2005).
[CrossRef]

Mersereau, R.

R. Mersereau, “The processing of hexagonally sampled two-dimensional signals,” Proc. IEEE 67, 930–949 (1979).
[CrossRef]

Miguel, E.

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.

Miyahara, M.

M. Miyahara, K. Kotani, and V. R. Algazi, “Objective picture quality scale for image coding,” IEEE Trans. Commun. 46, 1215–1226 (1998).
[CrossRef]

Moschetti, F.

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.

Olding, B.

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.

Ong, E.

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.

Pan, F.

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.

Papoulis, A.

A. Papoulis, “Generalized sampling theorem,” IEEE Trans. Circuit Syst. 24, 652–654 (1977).
[CrossRef]

Plataniotis, K. N.

R. Lukac and K. N. Plataniotis, “Color filter arrays: design and performance analysis,” IEEE Trans. Consum. Electron. 51, 1260–1267 (2005).
[CrossRef]

Ramanath, R.

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]

Sander, W. A.

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]

Sankur, B.

I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” J. Electron. Imaging 11, 206–223 (2002).
[CrossRef]

Sayood, K.

I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” J. Electron. Imaging 11, 206–223 (2002).
[CrossRef]

Sheikh, H. R.

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]

Simoncelli, E. P.

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]

Slavik, P.

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.

Snyder, W. E.

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]

Tropea, C.

R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007).
[CrossRef]

Vera-Perez, M.

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.

Wandell, B. A.

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.

Wang, Z.

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]

Wolfe, P. J.

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]

Yand, X.

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.

Yao, S.

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.

Zanoguera, B.

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.

Ziou, D.

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]

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]

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.

A. Horé and D. Ziou, “Image quality metrics: psnr vs. ssim,” in International Conference on Pattern Recognition (IEEE Computer Society, 2010), pp. 2366–2369.

Exp. Fluids (1)

R. Hain, C. J. Kahler, and C. Tropea, “Comparison of ccd, cmos and intensified cameras,” Exp. Fluids 42, 403–411 (2007).
[CrossRef]

IEEE Trans. Circuit Syst. (1)

A. Papoulis, “Generalized sampling theorem,” IEEE Trans. Circuit Syst. 24, 652–654 (1977).
[CrossRef]

IEEE Trans. Commun. (1)

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Figures (7)

Fig. 1.
Fig. 1.

Image formation process.

Fig. 2.
Fig. 2.

Examples of mosaics: (a) Bayer mosaic, (b) vertical stripes (TV) mosaic, (c) diagonal stripes mosaic, (d) HVS-based mosaic, (e) CYGM mosaic, (f) RGBE mosaic, (g) PenTile mosaic, (h) Burtoni mosaic, (i) Optimal mosaic Cfa4b [1], and (j) Kodak mosaic.

Fig. 3.
Fig. 3.

(a) Astatotilapia burtoni [12] and (b) a section of its retina [10].

Fig. 4.
Fig. 4.

Color aliasing with different CFA patterns: (a1) and (a2) original images, (b1) and (b2) demosaiced images obtained using the Bayer mosaic, (c1) and (c2) demosaiced images obtained using the diagonal stripes mosaic. (d1) and (d2) demosaiced images obtained using the vertical stripes mosaic.

Fig. 5.
Fig. 5.

Spectrum of a mosaiced image in the Y band: (a) Burtoni mosaic, (b) Bayer mosaic, (c) optimal mosaic Cfa4b [1].

Fig. 6.
Fig. 6.

Sample of each semantic set of images used in the experiments.

Fig. 7.
Fig. 7.

Parts of a CFA pattern (a) with only RGB primary colors and (b) with nonprimary colors.

Tables (6)

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Table 1. Aliasing Error : Percentage of Minimal Valuesa

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Table 2. Aliasing Error : Percentage of Maximal Values

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Table 3. Resolution Error : Percentage of Minimal Values

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Table 4. Resolution Error : Percentage of Maximal Values

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Table 5. Product of the Resolution and Aliasing Errors : Percentage of Minimal Values

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Table 6. Product of the Resolution and Aliasing Errors : Percentage of Maximal Values

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

{ E r = f s x / 2 f s x / 2 f s y / 2 f s y / 2 I c ( f x , f y ) I d ( f x , f y ) 2 d f x d f y E a = F a F a I c ( f x , f y ) I d ( f x , f y ) 2 d f x d f y .
f s x = 1 n x and f s y = 1 n y ,
M ( p , q ) = I ( p , q ) d ( p , q ) ,
M ( p , q ) k = I ( p , q ) k × d ( p , q ) k k = 1 3 d ( p , q ) k .

Metrics