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

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.