Benjamin Mathon

Multispectral demosaicing using intensity-based spectral correlation

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.

Multispectral Demosaicing Using Pseudo-Panchromatic Image

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.

A Quick Tour of Watermarking Techniques

In order to understand and analyse the main components of watermarking security presented in the next chapters, we introduce in this chapter the different elements needed to embed a watermark or a message inside a host content. We first present a functional view of a watermarking scheme (the embedding function, the decoding/detection function) and then its geometrical interpretation. Then we present the most popular class of watermarking schemes: spread-spectrum watermarking and watermarking techniques based on the idea of dirty paper codes.

Multispectral Demosaicing Using Intensity in Edge-Sensing and Iterative Difference-Based Methods

Single-sensor multispectral cameras, that sample spectral channels using a multispectral filter array, have recently emerged. They provide a raw image in which each channel is spectrally sampled pixel-wise according to the filter array pattern. A demosaicing procedure is then needed to estimate a multispectral image with full spectral resolution. The usefulness of intensity-based demosaicing has been shown in a previous work. In this paper, we both propose an intensity-based adaptation of the Binary Tree-based Edge-Sensing method for raw images with no dominant spectral band, and a new iterative method that uses the central wavelength distance between spectral bands.

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

Spatio-spectral binary patterns based on multispectral filter arrays for texture classification

To discriminate gray-level texture images, spatial texture descriptors can be extracted using the local binary pattern (LBP) operator. This operator has been extended to color images at the expense of increased memory and computation requirements. Some authors propose to compute texture descriptors directly from raw images provided through a Bayer color filter array, which both avoids the demosaicking step and reduces the descriptor size. Recently, multispectral snapshot cameras have emerged to sample more than three wavelength bands using a multispectral filter array. Such cameras provide a raw image in which a single spectral channel value is available at each pixel. In this paper we design a local binary pattern operator that jointly extracts the spatial and spectral texture information directly from a raw image. Extensive experiments on a large dataset show that the proposed descriptor has both reduced computation cost and high discriminative power with regard to classical LBP descriptors applied to demosaicked images.

Conclusions and Open Problems

This chapter concludes this book on Watermarking Security and I hope that the reader will have a better view of the ins and outs of this domain. If watermarking security may look like a cat and mice game that is never ending, we can however state several important conclusions derived for the large variety of researches that have been conducted but there are also fascinating related problems that still need to be solved. The goal of this last chapter is to list the different outputs and open questions related to watermarking security but also general security of information forensics.