Adaptive multiplicative-noise reduction in SAR images with polynomial transforms

The authors present an original method for restoring synthetic aperture radar (SAR) images via the polynomial transform. This transform is an image description model that incorporates important properties of visual perception, such as the Gaussian-derivative model of early vision. Based on this, the authors present a directional-sensitive technique that adapts the degree of noise reduction to the local contents of the image, i.e., to the presence of important image features and to the local noise statistics. The restored image is obtained by means of an inverse polynomial transform which consists of interpolating the transformed coefficients with pattern functions that are products of a polynomial and a window function. They show in this paper how the noise reduction task can be improved by detecting the position and orientation of relevant contours and considering the multiplicative nature of speckle in SAR images. This method is applied in a coarse-to-fine resolution approach, in which, contour location is not degraded even at the stage of high resolution processing