LAUSR

Speckle is a type of multiplicative noise that affects all coherent imaging modalities including synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality and can adversely affect the performance of SAR image applications such as automatic target recognition and change detection. Thus, SAR despeckling is an important problem in remote sensing. In this letter, we introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method uses a Markov chain that transforms clean images into white Gaussian noise by successively adding random noise. The despeckled image is obtained through a reverse process that predicts the added noise iteratively, using a noise predictor conditioned on the speckled image. In addition, we propose a new inference strategy based on cycle spinning to improve the despeckling performance. Our experiments on both synthetic and real SAR images demonstrate that the proposed method leads to significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods. The code is available at: https://github.com/malshaV/SAR_DDPM