A novel theory of SAR image restoration and enhancement with ICA

Active radar sensing is an important method of obtaining inventory information about remote and cloud-covered areas of the world. However, automatic interpretation of SAR images is often difficult due to speckle noise. Appearing as a random granular pattern, speckles seriously degrade the image quality and affect the task of human interpretation and scene analysis. For this kind of speckle removal problem, one of the difficulties is to overcome the tradeoff between noise reduction and preserving significant image details. A novel theory of SAR image restoration and enhancement with independent component analysis (ICA) is proposed. We assume that the speckle noise in SAR images comes from a different signal source, which accompanies but is independent (their statistical characteristics are not same.) of the "true signal source" (image details). Thus the speckle removal problem can also he described as "signal source separation" problem. Then in order to enhance the "true signal source", we classify the basis images and span them into two different signal subspaces, namely "true signal subspace" and "speckle subspace". Finally we build different nonlinear estimators in each signal subspace to recover the original image. In our experiments, the SAR images consist of nine channels of images. We compare our method with two other well known speckle reduction approaches (Kuan filter and Lee filter). The results show that with our method the speckle noise is efficiently removed while at the same time important details (edges in particular) are retained without introducing artificial structures. We further calculate the ratio of standard deviation to mean (SD/Mean) for each image and use it as a criterion for image quality and find that the improvement with our method is more evident for images with ''high level speckle noise".