A general framework for designing image processing algorithms for coherent polarimetric images

We describe a general framework to design optimal image processing algorithms for polarimetric images formed with coherent radiations, which can be optical or microwave. Starting from the classical speckle model for coherent signals, we show that a wide class of algorithms to perform such tasks as detection, localization and segmentation depend on a simple statistics, which is the determinant of the coherency matrix estimated on a given region of the image. We use this property to design computationally efficient techniques for target/edge detection and image segmentation using statistical active contours and the minimum description length principle.