SAR Image Denoising Using Adaptive Smoothing and Sparse Representation

Due to extensive SAR applications and the need to recognize SAR image details, the issue of improving the quality of these images after formation has been widely considered. Due to the nature of SAR image formation, the multiplicative speckle noise is considered as the most important factor in the quality degradation of these images. In this paper, a new method for removing speckle noise is presented. The main ideas of this article are using MAP estimator in accordance with the noise distribution function and presentation of a local convex optimization problem along with employment of adaptive smoothing, sparse representation regularizations and projection to the feature space. The local optimization model and adaptive smoothing provide proper noise removal and strong edges preservation and prevent image over smoothing. Also using sparse representation leads to texture preservation, and projection to the feature space enhances the algorithm against high noise levels. In order to solve the optimization problem, a method based on alternating minimization is introduced. The simulation results show good performance of the proposed method in noise reduction and preservation of image details which is better than many existing methods.