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.

Single-sensor color cameras, which classically use a color filter array (CFA) 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 (MSFA) that provides a raw image in which a single channel level is available at each pixel. A demosaicing procedure is then needed to estimate a multispectral image with full spectral resolution. In this paper, we propose a new demosaicing method that takes spectral and spatial correlations into account by estimating the level for each channel. Experimental results show that it provides estimated images of better quality than classical methods.

Texture description is a challenging problem with color images. Despite some attempts to include colors in local binary patterns (LBPs), no proposal has emerged as a color counterpart of grayscale LBPs. This is because colors are defined by vectors that are not naturally ordered and several ways exist to compare them. We propose an LBP extension that takes the vector information of color into account due to a color order. As several color orders are available and the selection of the most suitable one is difficult, we combine two of them in a texture descriptor called “mixed color order LBPs.” This small-size feature provides good performance on several benchmark databases for two classification problems with regard to larger-size LBP-based features of color textures.