Single-sensor color cameras, which classically use a color filter array to sample RGB channels, have recently been extended to the multispectral domain. To sample more than three wavelength bands, such systems use a multispectral filter array that provides a raw image in which a single channel value is available at each pixel. A demosaicing procedure is then needed to estimate a fully defined multispectral image. In this paper, we review multispectral demosaicing methods and propose a new one based on the pseudo-panchromatic image (PPI). Pixel values in the PPI are computed as the average spectral values. Experimental results show that our method provides estimated images of better quality than classical ones.

In this paper, we present a new approach for color texture classification by use of Haralick features extracted from co-occurrence matrices computed from local binary pattern (LBP) images. These LBP images, which are different from the color LBP initially proposed by Maenpaa and Pietikainen, are extracted from color texture images, which are coded in 28 different color spaces. An iterative procedure then selects among the extracted features, those which discriminate the textures, in order to build a low dimensional feature space. Experimental results, achieved with the BarkTex database, show the interest of this method with which a satisfying rate of well-classified images (85.6%) is obtained, with a 10-dimensional feature space.