摘要翻译：
科学成像中的许多材料和生物样品都具有非局部重复结构的特征。用扫描电镜和电子断层扫描对这些进行了研究。在2D图像采集几何中对单个像素进行稀疏采样，或者在层析成像实验中对具有大倾斜增量的投影图像进行稀疏采样，可以实现高速数据采集，并最大限度地减少电子束造成的样本损伤。本文提出了一种利用图像中的非局部冗余信息进行电子层析重建和稀疏图像插值的算法。我们采用了一种称为即插即用(P&P)先验的框架来解决正则化反演设置中的这些成像问题。P&P方法的威力在于它允许广泛的现代去噪算法作为层析成像和图像插值的“先验模型”。我们还给出了保证P&P方法收敛的充分数学条件，并利用这些见解设计了一种新的非局部均值去噪算法。最后，我们证明了该算法在模拟和真实电镜数据上都能获得更高质量的重建，并且与其他方法相比，它具有更好的收敛性。
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英文标题：
《Plug-and-Play Priors for Bright Field Electron Tomography and Sparse
  Interpolation》
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作者：
Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Lawrence F.
  Drummy, Jeffrey P. Simmons, Charles A. Bouman
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最新提交年份：
2015
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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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：
  Many material and biological samples in scientific imaging are characterized by non-local repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a 2D image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam.   In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the non-local redundancy in images. We adapt a framework, termed plug-and-play (P&P) priors, to solve these imaging problems in a regularized inversion setting. The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the P&P approach, and we use these insights to design a new non-local means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.
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PDF链接：
https://arxiv.org/pdf/1512.07331