SAS Image De-noising by the Curvelet Stochastic Matched Filter Transform
Philippe Courmontagne, Fabien Chaillan, Olivier Lerda · OCEANS 2006 - Asia Pacific · 2006
Over the past few years a great deal of research has been dedicated to SAS image de-noising. Indeed, such images are highly corrupted by a granular multiplicative noise, called the speckle noise, due to the construction of the image itself. This noise reduces spatial and radiometric resolutions. The goal of the research in the area of SAS image de-noising is double: on one hand to reduce the noise level and on the other hand to preserve the spatial resolution. In this context, recently a new approach has been developed: the curvelet transform. Such a method allows good spatial resolution preservation but the results are still slightly disturbed by the introduction of disturbing straight lines due to the process itself. The purpose of this article is to improve the de-noising power of the curvelet transform, while keeping its spatial resolution preservation. To achieve such an objective, the idea is to couple this transform with a filtering technique, known as the stochastic matched filtering method. Results obtained on real SAS data are presented and compared with those obtained using the classical curvelet transform.