Bayesian Despeckling of Structured Sources
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Abstract (arXiv)
Speckle noise is a fundamental challenge in coherent imaging systems, significantly degrading image quality. Over the past decades, numerous despeckling algorithms have been developed for applications such as Synthetic Aperture Radar (SAR) and digital holography. In this paper, we aim to establish a theoretically grounded approach to despeckling. We propose a method applicable to general structured stationary stochastic sources. We demonstrate the effectiveness of the proposed method on piecewise constant sources. Additionally, we theoretically derive a lower bound on the despeckling performance for such sources. The proposed depseckler applied to the 1-Markov structured sources achieves better reconstruction performance with no strong simplification of the ground truth signal model or speckle noise.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
9 of 32 references.
References · 9
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 533
- 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 364
- 2020Deep Learning Meets SARcited by 60
- 2017SAR image despeckling through convolutional neural networkscited by 30
- 2020Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networkscited by 28
- 2017SAR Image Despeckling Using a Convolutional Neural Networkcited by 24
- 2023SDDPM: Speckle Denoising Diffusion Probabilistic Modelscited by 6
- 2018DeepLSR: a deep learning approach for laser speckle reductioncited by 3
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Citing papers per year: 2025: 1.
1 paper.