Sid Ahmed Soumia, Messali Zoubeida, Boubchir Larbi, Bouridane Ahmed, Marco Sergio, Messaoudi Cédric
Faculty of Science and Technology, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria.
LIASD research Lab., Department of Computer Science, University of Paris 8, Saint-Denis, France.
BMC Biomed Eng. 2019 Jun 13;1:13. doi: 10.1186/s42490-019-0013-0. eCollection 2019.
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.
由于能量过滤透射电子显微镜（EFTEM）图像的断层扫描系列中存在高噪声水平，对齐和三维重建步骤变得非常困难。为了改进对齐过程，进而实现更准确、更好的三维断层扫描重建，应在EFTEM数据系列上应用预处理步骤。
在低信噪比下对真实EFTEM数据系列进行的实验表明，所提出的去噪方法具有可行性和准确性，可与现有的最佳泊松图像去噪方法相竞争。所提出的去噪方法的有效性得益于在具有尖锐频率定位域的轮廓波变换（CTSD）和方差稳定变换（VST）中使用非参数贝叶斯估计。此外，用于获得去噪图像最终估计值的最优逆安斯科姆变换实现了准确的断层扫描重建。
所提出的方法提供了关于所考虑样本上单个化学元素三维分布的定性信息。
