Abstract
Many coherent imaging modalities such as synthetic aperture radar suffer from a multiplicative noise, commonly referred to as speckle, which often makes the interpretation of data difficult. An effective strategy for speckle reduction is to use a dictionary that can sparsely represent the features in the speckled image. However, such approaches fail to capture important salient features such as texture. In this paper, we present a speckle reduction algorithm that handles this issue by formulating the restoration problem so that the structure and texture components can be separately estimated with different dictionaries. To solve this formulation, an iterative algorithm based on surrogate functionals is proposed. Experiments indicate the proposed method performs favorably compared to state-of-the-art speckle reduction methods.
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