Multiplicative-additive despeckling in SAR images

Visual and automatic analyses using synthetic aperture radar (SAR) images are challenging because of inherently formed speckle noise. Thus, reducing speckle noise in SAR images is an important research area for SAR image analysis. During speckle noise reduction, homogeneous regions should be smoothed while details such as edges and point scatterers need to be preserved. General speckle noise model contains gamma distributed multiplicative part which is dominant and Gaussian distributed additive part which is in low amount and mostly neglected in literature. In this study, a novel sparsity-driven speckle reduction method is proposed that takes both multiplicative noise model and additive noise model into consideration. The proposed speckle reduction method uses a cost function with multiplicative and additive data terms besides the total variation smoothness term. Also, an efficient and stable numerical minimization scheme is proposed for the proposed cost function that deals with multiplicative and additive noise. Speckle reduction performance of the proposed method is shown on synthetically generated SAR images and real-world SAR images

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