SAR image despeckling by combination of fractional-order total variation and nonlocal low rank regularization

This paper proposes a combinational regularization model for synthetic aperture radar (SAR) image despeckling. In contrast to most of the well-known regularization methods that only use one image prior property, the proposed combinational regularization model includes both fractional-order total variation (FrTV) regularization term and nonlocal low rank (NLR) regularization term. By characterizing the smoothness and nonlocal self-similarity property of the SAR image simultaneously, the proposed model, on the one hand, can better remove the noise in homogeneous regions of a noisy image, and on the other hand, can better preserve edges and geometrical features of the images during the despeckling process. Afterwards, an alternating direction method (ADM) is derived to efficiently solve the optimization problem in the proposed model. Experimental results demonstrate the good performance of the proposed model, both in removing SAR image speckles and preserving image texture and details.