A multigrid approach to image processing

A second order partial differential operator is applied to an image function. By using a multigrid operator known from the so-called approximation property, we derive a new type of multiresolution decomposition of the image. As an example, the Poisson case is treated in-depth. Using the new transform we devise an algorithm for image fusion. The actual recombination is performed on the image functions on which the partial differential operator has been applied first. A fusion example is elaborated upon. Other applications can be envisaged as well

A multigrid approach to image processing.

A second order partial differential operator is applied to an image function. By using a multigrid operator known from the so-called approximation property, we derive a new type of multiresolution decomposition of the image. As an example, the Poisson case is treated in-depth. Using the new transform we devise an algorithm for image fusion. The actual recombination is performed on the imagefunctions on which the partial differential operator has been applied first. A fusion example is elaborated upon. Other applications can be envisaged as wel

What multigrid and Poisson do to one's image

Though the pun in the title is intended, it is not quite fair to Piet Wesseling as he is a person who promoted the development of multigrid to far more complicated equations than the Poisson equation. Instead, the title should be taken more literally as it truly relates to the contents of this note. It is shown that while multigrid is renowned for his efficiency in solving partial differential equations, integral equations and what not, it can also, maybe surprisingly, be used for the multiresolution of images

Multimodality and Multiresolution Image Fusion

Standard multiresolution image fusion of multimodal images may yield an output image with artifacts due to the occurrence of opposite contrast in the input images. Equal but opposite contrast leads to noisy patches, instable with respect to slight changes in the input images. Unequal and opposite contrast leads to uncertainty of how to interpret the modality of the result. In this paper a biased fusion is proposed to remedy this, where the bias is towards one image, the so-called iconic image, in a preferred spectrum. A nonlinear fusion rule is proposed to prevent that the fused image reverses the local contrasts as seen in the iconic image. The rule involves saliency and a local match measure. The method is demonstrated by artificial and real-life examples

The Multigrid Image Transform

A second order partial differential operator is applied to an image function. To this end we consider both the Laplacian and a more general elliptic operator. By using a multigrid operator known from the so-called approximation property, we derive a multiresolution decomposition of the image without blurring of edges at coarser levels. We investigate both a linear and a nonlinear variant and compare to some established methods

The multigrid image transform.

A second order partial differential operator is applied to an image function. To this end we consider both the Laplacian and a more general elliptic operator. By using a multigrid operator known from the so-called approximation property, we derive a multiresolution decomposition of the image without blurring of edges at coarser levels. We investigate both a linear and a nonlinear variant and compare to some established method

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

3D modelling using partial differential equations (PDEs).

Partial differential equations (PDEs) are used in a wide variety of contexts in computer science ranging from object geometric modelling to simulation of natural phenomena such as solar flares, and generation of realistic dynamic behaviour in virtual environments including variables such as motion, velocity and acceleration. A major challenge that has occupied many players in geometric modelling and computer graphics is the accurate representation of human facial geometry in 3D. The acquisition, representation and reconstruction of such geometries are crucial for an extensive range of uses, such as in 3D face recognition, virtual realism presentations, facial appearance simulations and computer-based plastic surgery applications among others. The principle aim of this thesis should be to tackle methods for the representation and reconstruction of 3D geometry of human faces depending on the use of partial differential equations and to enable the compression of such 3D data for faster transmission over the Internet. The actual suggested techniques are based on sampling surface points at the intersection of horizontal and vertical mesh cutting planes. The set of sampled points contains the explicit structure of the cutting planes with three important consequences: 1) points in the plane can be defined as a one dimensional signal and are thus, subject to a number of compression techniques; 2) any two mesh cutting planes can be used as PDE boundary conditions in a rectangular domain; and 3) no connectivity information needs to be coded as the explicit structure of the vertices in 3D renders surface triangulation a straightforward task. This dissertation proposes and demonstrates novel algorithms for compression and uncompression of 3D meshes using a variety of techniques namely polynomial interpolation, Discrete Cosine Transform, Discrete Fourier Transform, and Discrete Wavelet Transform in connection with partial differential equations. In particular, the effectiveness of the partial differential equations based method for 3D surface reconstruction is shown to reduce the mesh over 98.2% making it an appropriate technique to represent complex geometries for transmission over the network

A multigrid approach to image processing.

A second order partial differential operator is applied to an image function. By using a multigrid operator known from the so-called approximation property, we derive a new type of multiresolution decomposition of the image. As an example, the Poisson case is treated in-depth. Using the new transform we devise an algorithm for image fusion. The actual recombination is performed on the imagefunctions on which the partial differential operator has been applied first. A fusion example is elaborated upon. Other applications can be envisaged as wel

A multigrid approach to image processing

A second order partial differential operator is applied to an image function. By using a multigrid operator known from the so-called approximation property, we derive a new type of multiresolution decomposition of the image. As an example, the Poisson case is treated in-depth. Using the new transform we devise an algorithm for image fusion. The actual recombination is performed on the image functions on which the partial differential operator has been applied first. A fusion example is elaborated upon. Other applications can be envisaged as well