基于贝叶斯框架，提出适用于结构化源的去斑方法，理论性能有保障。
Bayesian Despeckling of Structured Sources
- 采用贝叶斯推断建模结构化随机源，无需简化真实信号与噪声模型。
- 在1-Markov结构源上实现更优重建性能，优于现有方法。
- 首次理论推导此类源去斑的性能下界，为算法评估提供基准。
相干成像系统中的斑点噪声是图像质量退化的根本挑战。过去几十年中，已开发出多种去斑算法，广泛应用于合成孔径雷达（SAR）和数字全息等领域。本文旨在建立一种理论严谨的去斑方法。我们提出一种适用于广义结构化平稳随机源的方法，并在分段常数源上验证其有效性。此外，我们理论上推导了此类源去斑性能的下界。所提去斑器应用于1-Markov结构源时，在不强加真实信号模型或斑点噪声简化假设的前提下，实现了更优的重建效果。
原文摘要 · Abstract (English)
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.
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