The role of prior knowledge in coherent image processing

Abstract

Models that encode prior knowledge about a scene provide a means for interpreting image data from that scene in more detail than would otherwise be so. Information about both background clutter and target characteristics should be included in this prior knowledge. We demonstrate the use of a generalized noise model to represent a variety of naturally occurring random terrain clutter textures observed in high-resolution synthetic aperture radar (SAR) images. In addition a similar approach is adopted for the simulation of such textures. Having established the background properties we next introduce prior knowledge about any target within the scene and exploit this in achieving a cross-section reconstruction having improved resolution compared with the original image. Examples of such a super-resolution method based on singular value decomposition are demonstrated and the limits of the technique are indicated.