Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge. This is due in the main to the fact that current estimation procedures consist of performing linear regressions across frequency scales of the 2D dyadic wavelet transform, for which only a few such scales are computable for images. The strongly non-Gaussian nature of multifractal processes, combined with their complicated dependence structure, makes it difficult to develop suitable models for parameter estimation. Here, we propose a Bayesian procedure that addresses the difficulties in the estimation of the multifractality parameter. The originality of the procedure is threefold. The construction of a generic semiparametric statistical model for the logarithm of wavelet leaders; the formulation of Bayesian estimators that are associated with this model and the set of parameter values admitted by multifractal theory; the exploitation of a suitable Whittle approximation within the Bayesian model which enables the otherwise infeasible evaluation of the posterior distribution associated with the model. Performance is assessed numerically for several 2D multifractal processes, for several image sizes and a large range of process parameters. The procedure yields significant benefits over current benchmark estimators in terms of estimation performance and ability to discriminate between the two most commonly used classes of multifractal process models. The gains in performance are particularly pronounced for small image sizes, notably enabling for the first time the analysis of image patches as small as 64 × 64 pixels.

Pansharpening first aims at fusing a panchromatic image with a multispectral image to generate an image with the high spatial resolution of the former and the spectral resolution of the latter. In the last decade many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems these methods are now extending to hyperspectral images. In this work, we attempt to compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Nine methods from different classes are analysed: component substitution, multiresolution analysis, hybrid, Bayesian and matrix decomposition approaches. These techniques are evaluated with the Wald’s Procol on one dataset to characterize their performances and their robustness.

We develop a representation of reverse-time migration (RTM) in terms of Fourier integral operators, the canonical relations of which are graphs. Through the dyadic parabolic decomposition of phase space, we obtain the solution of the wave equation with a boundary source and homogeneous initial conditions using wave packets. On this basis, we develop a numerical procedure for the reverse-time continuation from the boundary of scattering data and for RTM. The algorithms are derived from those we recently developed for the discrete approximate evaluation of the action of Fourier integral operators and inherit their conceptual and numerical properties.

Dictionary learning is a powerful approach for sparse representation. However, the numerical complexity of classical dictionary learning methods restricts their use to atoms with small supports such as patches. In a previous work, we introduced a model based on a composition of convolutions with sparse kernels to build large dictionary atoms with a low computational cost. The subject of this work is to consider this model at the next level, i.e., to build a full dictionary of atoms from convolutions of sparse kernels. Moreover, we further reduce the size of the representation space by organizing the convolution kernels used to build atoms into a tree structure. The performance of the method is tested for the construction of a curvelet dictionary with a known code.

Abstract In this paper, we study the problem of gateway placement in an hybrid mobile ad hoc-satellite network. We propose a genetic algorithm based approach to solve this multi-criteria optimization problem. The analysis of the proposed algorithm is made by means of simulations. Topology dynamics are also taken into account since the node mobility will impact the gateway placement decisions. Our solution shows promising results and displays unmatched flexibility with respect to the optimization criteria.

This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods.

In this paper, we provide a proof for the conjecture made by Abu Surra et al. [1] to simplify the computation of ensemble input output weight enumerators for protograph-based low density parity check (LDPC) codes. Furthermore, we propose a new method to compute more efficiently the ensemble weight enumerator. This approach can be applied particularly to lighten the computations for high rate codes, generalized LDPC codes or spatially coupled LDPC codes.

Remote sensing images are images of the Earth acquired from planes or satellites. Many different sensors have been developed to image the earth surface, including optical, SAR and hyperspectral images. Change detection on datasets of multitemporal images is one of the main interest of these images. The case where the dataset consist of images acquired by the same sensor has been thoroughly studied, however, dealing with heterogeneous images is very common nowadays. To deal with these images, we proposed a statistical model which describe the joint distribution of their pixel intensity. On unchanged areas, we expect the parameter vector of the model to belong to a manifold. The distance of the model parameter to the manifold can be thus be used as a similarity measure, and the manifold can be learned using images where no changes are present. In this talk I will present the statistical model, its parameter estimation, and the manifold learning approach. The results obtained with this method will be compared with those of other classical similarity measures.