Inversion of imaging mechanism by regularization of inverse volterra models
Inversion of imaging mechanism by regularization of inverse volterra models
Résumé
An imaging mechanism is defined as the process by which a sensor obtains an image of a physical reality. The inversion of the imaging mechanism can be a solution when one needs to measure some physical magnitude from imaging data. In this paper we propose a general method to model and invert such imaging mechanisms. Our approach consists of building a forward model based on the knowledge of the physical properties of the mechanism. Since imaging mechanisms can be nonlinear, we introduce the Volterra series expansion as a tool for the modeling. In order to illustrate our approach, we apply this technique to the estimation of underwater bottom topography using synthetic aperture radar images of the ocean surface.
An imaging mechanism is defined as the process by which a sensor obtains an image of a physical reality. The inversion of the imaging mechanism can be a solution when one needs to measure some physical magnitude from imaging data. In this paper we propose a general method to model and invert such imaging mechanisms. Our approach consists of building a forward model based on the knowledge of the physical properties of the mechanism. Since imaging mechanisms can be nonlinear, we introduce the Volterra series expansion as a tool for the modeling. In order to illustrate our approach, we apply this technique to the estimation of underwater bottom topography using synthetic aperture radar images of the ocean surface