Multispectral cameras sample the visible and/or the infrared spectrum according to narrow spectral bands. Available technologies include snapshot multispectral cameras equipped with filter arrays that acquire raw images at video rate. Raw images require a demosaicing procedure to estimate a multispectral image with full spatio-spectral definition. In this manuscript we review multispectral demosaicing methods and propose a new one based on the pseudo-panchromatic image estimated directly from the raw image. We highlight the influence of illumination on demosaicing performances, then we propose pre- and post-processing normalization steps that make demosaicing robust to acquisition properties. Experimental results show that our method provides estimated images of better objective quality than classical ones and that normalization steps improve the quality of state-of-the- art demosaicing methods on images acquired under various illuminations. Multispectral images can be used for texture classification. To perform texture analysis we consider local binary pattern operators that extract texture descriptors from color texture images. We extend these operators to multispectral texture images at the expense of increased memory and computation requirements. We propose to compute texture descriptors directly from raw images, which both avoids the demosaicing step and reduces the descriptor size. For this purpose, we design a local binary pattern operator that jointly extracts the spatial and spectral texture information from a raw image. In order to assess classification on multispectral images we have proposed the first multispectral database of close-range textures in the visible and near infrared spectral domains. Extensive experiments on this database show that the proposed descriptor has both reduced computational cost and high discrim- inating power with regard to classical local binary pattern descriptors applied to demosaiced images.
Ludovic MACAIRE, Université de Lille, Directeur de thèse Sylvie TREUILLET, Polytech'Orléans, Rapporteur Yannick BERTHOUMIEU, Université de Bordeaux, Rapporteur Christine FERNANDEZ-MALOIGNE, Université de Poitiers, Examinateur Olivier LOSSON, Université de Lille, Examinateur Benjamin MATHON, Université de Lille, Examinateur Pierre CHAINAIS, Centrale Lille, Examinateur Jean-Baptiste THOMAS, Université de Bourgogne, Examinateur
Thesis of the team Imagerie Couleur defended on 22/11/2018