SALIENCE PRESERVING IMAGE FUSION WITH DYNAMIC RANGE COMPRESSION

Gradient conveys important salient features in images. Traditional fusion methods based on gradient generally treat gradients from multichannels as a multi-valued vector, and compute its global statistics under the assumption of identical distribution. However, different source channels may reflect different important salient features, and their gradients are basically non-identically distributed. This prevents existing methods from successful salience preservation. In this paper, we propose to fuse the gradients from multi-channels in the concept of saliency. We first measure the salience map of each channel’s gradient, and then use their saliency to weight their contribution in computing the global statistics. Gradients with high saliency are properly highlighted in the target gradient, and thereby salient features in the sources are well preserved. Furthermore, we handle the dynamic range problem by applying range compression on the target gradient, and thereby halo effect is effectively reduced. 1

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