摘要翻译：
从目标的无相散斑图中恢复目标的图像是困难的。在多重散射介质成像中，传输矩阵是未知的。双相位恢复是最近提出的一种有效的方法，它通过相位恢复两步从无相位测量中恢复未知目标。本文将双相位反演中的两个步骤结合起来，构造了一个端到端的神经网络TCNN（Transforming Colvolutional neural network)，它直接学习无相位测量值与目标之间的关系。TCNN包含一个特殊的层，称为转换层，它旨在成为不同转换域之间的桥梁。通过引用{Metzler2017Coherence}中提供的经验数据验证，与现有的方法相比，TCNN可以获得相当的图像恢复质量。一旦TCNN参数稳定，不仅无需计算传输矩阵，而且可以大大缩短目标恢复的时间。
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英文标题：
《Multiple Scattering Media Imaging via End-to-End Neural Network》
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作者：
Ziyang Yuan and Hongxia Wang
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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：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。
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英文摘要：
  Recovering the image of an object from its phaseless speckle pattern is difficult. Let alone the transmission matrix is unknown in multiple scattering media imaging. Double phase retrieval is a recently proposed efficient method which recovers the unknown object from its phaseless measurements by two steps with phase retrieval.   In this paper, we combine the two steps in double phase retrieval and construct an end-to-end neural network called TCNN(Transforming Convolutional Neural Network) which directly learns the relationship between the phaseless measurements and the object. TCNN contains a special layer called transform layer which aims to be a bridge between different transform domains. Tested by the empirical data provided in\cite{Metzler2017Coherent}, images can be recovered by TCNN with comparable quality compared with state-of-the-art methods. Not only the transmission matrix needn't to be calculated but also the time to recover the object can be hugely reduced once the parameters of TCNN are stable.
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PDF链接：
https://arxiv.org/pdf/1806.09968