SAR Despeckling using a Denoising Diffusion Probabilistic Model
This addresses speckle noise degradation in SAR images for remote sensing applications like target recognition and change detection, representing an incremental advancement using a diffusion model.
The paper tackles SAR image despeckling by introducing SAR-DDPM, a denoising diffusion probabilistic model, and demonstrates significant improvements in quantitative and qualitative results over state-of-the-art methods.
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.