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摘要翻译：
相干衍射成像(CDI)利用X射线和电子进行成像，在过去的二十年里取得了非常迅速的进展。相关的重建算法通常是迭代的，并加入了原始的第一次估计。布拉格相干衍射成像的确定性方法（Pavlov et al.，Sci.Rep.7，1132(2017))被用作收缩-包裹迭代重建过程的更精细的起点。与作为起点的自相关函数进行适当的比较。利用实空间误差度量和傅里叶空间误差度量分析了含噪和无噪模拟数据重建过程的收敛性。我们的结果表明，使用确定性-CDI重建作为后续迭代-CDI细化的种子，可以提高收敛速度和收敛程度，而不是目前常用的更粗糙的种子。我们还强调了在迭代细化的上下文中监视多个错误度量的效用。
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英文标题：
《Deterministic X-ray Bragg coherent diffraction imaging as a seed for
subsequent iterative reconstruction》
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作者：
Konstantin M. Pavlov, Kaye S. Morgan, Vasily I. Punegov, David M.
Paganin
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Coherent diffractive imaging (CDI), using both X-rays and electrons, has made extremely rapid progress over the past two decades. The associated reconstruction algorithms are typically iterative, and seeded with a crude first estimate. A deterministic method for Bragg Coherent Diffraction Imaging (Pavlov et al., Sci. Rep. 7, 1132 (2017)) is used as a more refined starting point for a shrink-wrap iterative reconstruction procedure. The appropriate comparison with the autocorrelation function as a starting point is performed. Real-space and Fourier-space error metrics are used to analyse the convergence of the reconstruction procedure for noisy and noise-free simulated data. Our results suggest that the use of deterministic-CDI reconstructions, as a seed for subsequent iterative-CDI refinement, may boost the speed and degree of convergence compared to the cruder seeds that are currently commonly used. We also highlight the utility of monitoring multiple error metrics in the context of iterative refinement.
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PDF链接：
https://arxiv.org/pdf/1806.09465
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