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. In our model, no demosaicing algorithm is used, which makes it independent of such algorithms. We also use 11 semantic sets of color images in order to highlight the images 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 9 semantic sets which are the high frequency, aerial, indoor, face, aquatic, bright, dark, step and line classes.
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Couillaud, J., Horé, A., Ziou, D. (2012). Improving Image Acquisition: A Fish-Inspired Solution. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_12
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DOI: https://doi.org/10.1007/978-3-642-33140-4_12
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