论文标题

没有清洁数据的斑点图像修复

Speckle Image Restoration without Clean Data

论文作者

Tai, Tsung-Ming, Jhang, Yun-Jie, Hwang, Wen-Jyi, Cheng, Chau-Jern

论文摘要

斑点噪声是连贯成像系统中的固有干扰,例如数字全息,合成孔径雷达,光学相干断层扫描或超声系统。这些系统通常仅产生同一兴趣对象的每个观察角度观察,从而施加了在观测值之间利用统计数的困难。我们提出了一种新的图像恢复算法,该算法可以在没有清洁数据的情况下执行斑点噪声,并且不需要以相同的视角进行多个嘈杂的观察。我们提出的方法也可以应用于情况,而不知道先验的噪声分布。我们证明我们的方法特别适合通过首先验证合成数据集,并应用于现实世界的数字全息样本。与多个广泛施加的基线相比,定量测量和视觉检查的结果均优异。我们的方法甚至显示出不同斑点噪声强度的有希望的结果,而无需干净的数据。

Speckle noise is an inherent disturbance in coherent imaging systems such as digital holography, synthetic aperture radar, optical coherence tomography, or ultrasound systems. These systems usually produce only single observation per view angle of the same interest object, imposing the difficulty to leverage the statistic among observations. We propose a novel image restoration algorithm that can perform speckle noise removal without clean data and does not require multiple noisy observations in the same view angle. Our proposed method can also be applied to the situation without knowing the noise distribution as prior. We demonstrate our method is especially well-suited for spectral images by first validating on the synthetic dataset, and also applied on real-world digital holography samples. The results are superior in both quantitative measurement and visual inspection compared to several widely applied baselines. Our method even shows promising results across different speckle noise strengths, without the clean data needed.

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