SAR Despeckling using a Denoising Diffusion Probabilistic Model

Autor:	Perera, Malsha V., Nair, Nithin Gopalakrishnan, Bandara, Wele Gedara Chaminda, Patel, Vishal M.
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1109/LGRS.2023.3270799
Popis:	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. Comment: Our code is available at https://github.com/malshaV/SAR_DDPM
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.04514 Zobrazit plný text záznamu View this record from Arxiv