  Speckle is a multiplicative noise which affects all coherent imaging
modalities including Synthetic Aperture Radar (SAR) images. The presence of
speckle degrades the image quality and adversely affects the performance of SAR
image understanding applications such as automatic target recognition and
change detection. Thus, SAR despeckling is an important problem in remote
sensing. In this paper, we introduce SAR-DDPM, a denoising diffusion
probabilistic model for SAR despeckling. The proposed method comprises of a
Markov chain that transforms clean images to white Gaussian noise by repeatedly
adding random noise. The despeckled image is recovered by a reverse process
which iteratively predicts the added noise using a noise predictor which is
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 achieves significant improvements in both quantitative and qualitative
results over the state-of-the-art despeckling methods.
