Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging

A new fused Bayesian maximum entropy–variational analysis (BMEVA) method for enhanced radar/synthetic aperture radar (SAR) imaging is addressed as required for high-resolution remote sensing (RS) imagery. The variational analysis (VA) paradigm is adapted via incorporating the image gradient flow norm preservation into the overall reconstruction problem to control the geometrical properties of the desired solution. The metrics structure in the corresponding image representation and solution spaces is adjusted to incorporate the VA image formalism and RS model-level considerations; in particular, system calibration data and total image gradient flow power constraints. The BMEVA method aggregates the image model and system-level considerations into the fused SSP reconstruction strategy providing a regularized balance between the noise suppression and gained spatial resolution with the VA-controlled geometrical properties of the resulting solution. The efficiency of the developed enhanced radar imaging approach is illustrated through the numerical simulations with the real-world SAR imagery.Cinvesta

Multiframe Selective Information Fusion from Robust Error Estimation Theory

A dynamic procedure for selective information fusion from multiple image frames is derived from robust error estimation theory. The fusion rate is driven by the anisotropic gain function, defined to be the difference between the Gaussian smoothed-edge maps of a given input frame and of an evolving synthetic output frame. The gain function achieves both selection and rapid fusion of relatively sharper features from each input frame compared to the synthetic frame. Effective applications are demonstrated for image sharpening in imaging through atmospheric turbulence, for multispectral fusion of the RGB spectral components of a scene, for removal of blurred visual obstructions from in front of a distant focused scene, and for high-resolution two-dimensional display of three-dimensional objects in microscopy

Modèles de fusion et diffusion par équations aux dérivées partielles (application à la sismique azimutale)

Ce mémoire porte sur le développement de nouvelles méthodes de fusion d images à partir d un formalisme à base d Equations aux Dérivées Partielles (EDP). Les deux premiers chapitres bibliographiques portent sur les 2 domaines au centre de notre problématique : la fusion et les EDP. Le Chapitre 3 est consacré à la présentation progressive de notre modèle EDP de fusion constitué par un terme de fusion (diffusion inverse isotrope) et un terme de régularisation. De plus, un des attraits de l approche EDP est de pouvoir traiter avec le formalisme des données bruitées. L association d un terme de diffusion dépendant du type de données à traiter est donc abordée. Le chapitre 4 est consacré à l application des modèles de fusion-diffusion aux données sismiques. Pour répondre aux besoins de filtrage de ces données sismiques, nous proposons deux méthodes originales de diffusion 3D. Nous présenterons dans ce mémoire l approche de fusion 3D intégrant une de ces méthodes nommée SFPD (Seismic Fault Preserving Diffusion).This thesis focuses on developing new methods for image fusion based on Partial Differential Equations (PDE). The starting point of the proposed fusion approach is the enhancement process contained in most classical diffusion models. The aim of enhancing contours is similar to one of the purpose of the fusion: the relevant information (equivalent to the contours) must be found in the output image. In general, the contour enhancement uses an inverse diffusion equation. In our model of fusion, the evolution of each input image is led by such equation. This single equation must necessarily be accompanied by a global information detector useful to select the signal to be injected. In addition, an inverse diffusion equation, like any Gaussian deconvolution, raises problems of stability and regularization of the solution. To resolve these problems, a regularization term is integrated into the model. The general model of fusion is finally similar to an evolving cooperative system, where the information contained in each image starts moving towards relevant information, leading to a convergent process. The essential interest of PDE approach is to deal with noisy data by combining in a natural way two processes: fusion and diffusion. The fusion-diffusion proposed model is easy to adapt to different types of data by tuning the PDE. In order to adapt the fusion-diffusion model to a specific application, I propose 2 diffusion models: Seismic fault preserving diffusion and 3D directional diffusion . The aim is to denoise 3D seismic data. These models are integrated into the fusion-diffusion approach. One of them is successfully transferred to the industrial partner: french oil company Total. The efficiency of our models (fusion and fusion-diffusion) is proven through an experimental plan in both noisy and noisy-free data.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF