摘要翻译：
根据强度测量确定波的相位在电子显微镜、可见光光学和医学成像等领域有许多应用。基于传播的相位恢复，即从散焦图像中获得相位，显示了重要的前景。然而，这些方法在检索阶段的准确性方面存在局限性。误差来源包括散粒噪声、图像失准和衍射伪影。我们探索使用人工神经网络(ANNs)来提高应用于模拟强度测量的基于传播的相位恢复算法的精度。我们采用一种基于强度输运方程的相位恢复算法，从程序生成的样品的模拟显微照片中获得相位。然后，我们训练一个神经网络与对检索和精确的相位，并使用训练的神经网络处理一个测试集检索的相位图。该方法大大减小了相位中的总误差。我们还讨论了对这项工作的各种潜在的扩展。
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英文标题：
《Propagation based phase retrieval of simulated intensity measurements
  using artificial neural networks》
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作者：
Zachary David Cleary Kemp
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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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一级分类：Physics        物理学
二级分类：Optics        光学
分类描述：Adaptive optics. Astronomical optics. Atmospheric optics. Biomedical optics. Cardinal points. Collimation. Doppler effect. Fiber optics. Fourier optics. Geometrical optics (Gradient index optics. Holography. Infrared optics. Integrated optics. Laser applications. Laser optical systems. Lasers. Light amplification. Light diffraction. Luminescence. Microoptics. Nano optics. Ocean optics. Optical computing. Optical devices. Optical imaging. Optical materials. Optical metrology. Optical microscopy. Optical properties. Optical signal processing. Optical testing techniques. Optical wave propagation. Paraxial optics. Photoabsorption. Photoexcitations. Physical optics. Physiological optics. Quantum optics. Segmented optics. Spectra. Statistical optics. Surface optics. Ultrafast optics. Wave optics. X-ray optics.
自适应光学。天文光学。大气光学。生物医学光学。基本点。准直。多普勒效应。纤维光学。傅里叶光学。几何光学（梯度折射率光学、全息术、红外光学、集成光学、激光应用、激光光学系统、激光、光放大、光衍射、发光、微光学、纳米光学、海洋光学、光学计算、光学器件、光学成像、光学材料、光学计量学、光学显微镜、光学特性、光学信号处理、光学测试技术、光波传播、傍轴光学、光吸收、光激发、物理光学、生理光学、量子光学、分段光学、光谱、统计光学、表面光学、超快光学、波动光学、X射线光学。
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英文摘要：
  Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.
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PDF链接：
https://arxiv.org/pdf/1709.0994