Due to the presence of high noise level in tomographic series of energy filtered transmission electron microscopy (EFTEM) images, alignment and 3D reconstruction steps become so difficult. To improve the alignment process which will in turn allow a more accurate and better three dimensional tomography reconstructions, a preprocessing step should be applied to the EFTEM data series. Experiments with real EFTEM data series at low SNR, show the feasibility and the accuracy of the proposed denoising approach being competitive with the best existing methods for Poisson image denoising. The effectiveness of the proposed denoising approach is thanks to the use of a nonparametric Bayesian estimation in the Contourlet Transform with Sharp Frequency Localization Domain (CTSD) and variance stabilizing transformation (VST). Furthermore, the optimal inverse Anscome transformation to obtain the final estimate of the denoised images, has allowed an accurate tomography reconstruction. The proposed approach provides qualitative information on the 3D distribution of individual chemical elements on the considered sample.

中文翻译：

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使用带有锐频率定位的 Contourlet 变换基于方差稳定的迭代贝叶斯去噪：应用于 EFTEM 图像。

由于能量过滤透射电子显微镜 (EFTEM) 断层扫描系列图像中存在高噪声水平，对齐和 3D 重建步骤变得非常困难。为了改进对齐过程，从而允许更准确和更好的三维断层扫描重建，应将预处理步骤应用于 EFTEM 数据系列。在低信噪比下使用真实 EFTEM 数据系列进行的实验表明，所提出的去噪方法的可行性和准确性与现有的最佳泊松图像去噪方法具有竞争力。所提出的去噪方法的有效性归功于在具有锐频率定位域 (CTSD) 的 Contourlet 变换和方差稳定变换 (VST) 中使用非参数贝叶斯估计。此外，获得去噪图像的最终估计的最优 Anscome 逆变换允许精确的断层扫描重建。所提出的方法提供了有关样品上单个化学元素的 3D 分布的定性信息。