In coherent imaging systems, speckle is a signal-dependent noise that visually strongly degrades images' appearance. A huge amount of SAR data has been acquired from different sensors with different wavelengths, resolutions, incidences and polarizations. We extend the nonlocal filtering strategy to the temporal domain and propose a patch-based adaptive temporal filter (PATF) to take advantage of well-registered multi-temporal SAR images. A patch-based generalised likelihood ratio test is processed to suppress the changed object effects on the multitemporal denoising results. Then, the similarities are transformed into corresponding weights with an exponential function. The denoised value is calculated with a temporal weighted average. Spatial adaptive denoising methods can improve the patch-based weighted temporal average image when the time series is limited. The spatial adaptive denoising step is optional when the time series is large enough. Without reference image, we propose using a patch-based auto-covariance residual evaluation method to examine the ratio image between the noisy and denoised images and look for possible remaining structural contents. It can process automatically and does not rely on a supervised selection of homogeneous regions. It also provides a global score for the whole image. Numerous results demonstrate the effectiveness of the proposed time series denoising method and the usefulness of the residual evaluation method.

翻译：在相干成像系统中，散斑是一种信号相关噪声，会从视觉上严重劣化图像的外观。大量SAR数据已从不同波长、分辨率、入射角和极化方式的不同传感器获取。我们将非局部滤波策略扩展到时间域，并提出一种基于分块的自适应时间滤波器（PATF），以充分利用配准良好的多时相SAR图像。通过处理基于分块的广义似然比检验，抑制变化目标对多时相去噪结果的影响。随后，利用指数函数将相似性转化为对应权重，并采用时间加权平均计算去噪值。当时间序列有限时，空间自适应去噪方法可改善基于分块的加权时间平均图像；当时间序列足够长时，空间自适应去噪步骤为可选。在无参考图像的情况下，我们提出使用基于分块的自协方差残差评估方法，检验含噪图像与去噪图像之间的比值图像，并寻找可能残留的结构内容。该方法可自动处理，无需监督选择同质区域，同时能为整幅图像提供全局评分。大量结果验证了所提时间序列去噪方法的有效性以及残差评估方法的实用性。