摘要翻译：
能量大于50keV的相干X射线光子为利用布拉格峰位相恢复方法成像体晶体材料中的纳米级晶格畸变提供了新的可能性。然而，在高能量下，互易空间的压缩通常导致面探测器上分辨率较差的条纹，使得衍射数据不适用于紧凑晶体的三维重建。针对这一问题，我们提出了一种在散射强度中恢复精细条纹细节的方法。该恢复分为两个步骤：通过面探测器的面内亚像素运动进行多个欠采样测量，然后将该数据集传递给基于稀疏性的数值求解器，该数值求解器恢复适合于紧凑单晶标准布拉格相干衍射成像(BCDI)重建方法的条纹细节。本文的主要观点是，BCDI数据集的稀疏性可以通过识别探测器中的信号尽管分辨率很低，但是带限的来加强。这需要较少的平面内探测器平移来完全恢复信号，同时遵守信息论的限制。我们使用模拟的BCDI数据集来演示该方法，概述我们的稀疏恢复策略，并评论未来的机会。
---
英文标题：
《Sparse recovery of undersampled intensity patterns for coherent
  diffraction imaging at high X-ray energies》
---
作者：
Siddharth Maddali, Irene Calvo-Almazan, Jonathan Almer, Peter Kenesei,
  Jun-Sang Park, Ross Harder, Youssef Nashed, Stephan Hruszkewycz
---
最新提交年份：
2017
---
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：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--
一级分类：Physics        物理学
二级分类：Materials Science        材料科学
分类描述：Techniques, synthesis, characterization, structure.  Structural phase transitions, mechanical properties, phonons. Defects, adsorbates, interfaces
技术，合成，表征，结构。结构相变，力学性质，声子。缺陷，吸附质，界面
--
一级分类：Physics        物理学
二级分类：Instrumentation and Detectors        仪器仪表和探测器
分类描述：Instrumentation and Detectors for research in natural science, including optical, molecular, atomic, nuclear and particle physics instrumentation and the associated electronics, services, infrastructure and control equipment.
用于自然科学研究的仪器和探测器，包括光学、分子、原子、核和粒子物理仪器和相关的电子学、服务、基础设施和控制设备。
--

---
英文摘要：
  Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this data set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. We use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.
---
PDF链接：
https://arxiv.org/pdf/1712.01108