摘要翻译：
通过强散射介质进行光学聚焦和成像是一项具有挑战性的任务，但从科学研究到生物医学应用和日常生活都有着广泛的应用。利用散斑强度相关的记忆效应(ME)，只需使用一个单次散斑图就可以实现高质量的目标恢复，并避免了一些减少散射效应的复杂步骤。尽管大目标的所有空间信息都被嵌入到单个散斑图像中，但ME对通过散射介质成像的视场(FOV)有严格的限制。超出ME区域的对象无法恢复，只会产生不需要的散斑模式，导致散斑对比度和恢复质量的降低。在这里，我们利用这些不可避免的散斑图提取空间信息，扩大光学成像系统的视场。区域点扩展函数是固定的，每次只需记录一次，通过反卷积算法恢复目标对应的空间区域。然后在迭代过程中进行自动加权平均，得到视场显著增大的目标。我们的结果是朝着强散射介质的先进成像技术迈出的重要一步。
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英文标题：
《Single shot large field of view imaging with scattering media by spatial
  demultiplexing》
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作者：
Sujit Kumar Sahoo, Dongliang Tang, Cuong Dang
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最新提交年份：
2017
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分类信息：

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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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：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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一级分类：Physics        物理学
二级分类：Applied Physics        应用物理学
分类描述：Applications of physics to new technology, including electronic devices, optics, photonics, microwaves, spintronics, advanced materials, metamaterials, nanotechnology, and energy sciences.
物理学在新技术中的应用，包括电子器件、光学、光子学、微波、自旋电子学、先进材料、超材料、纳米技术和能源科学。
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英文摘要：
  Optically focusing and imaging through strongly scattering media are challenging tasks but have widespread applications from scientific research to biomedical applications and daily life. Benefiting from the memory effect (ME) for speckle intensity correlations, only one single-shot speckle pattern can be used for the high quality recovery of the objects and avoiding some complicated procedures to reduce scattering effects. In spite of all the spatial information from a large object being embedded in a single speckle image, ME gives a strict limitation to the field of view (FOV) for imaging through scattering media. Objects beyond the ME region cannot be recovered and only produce unwanted speckle patterns, causing reduction in the speckle contrast and recovery quality. Here, we extract the spatial information by utilizing these unavoidable speckle patterns, and enlarge the FOV of the optical imaging system. Regional point spreading functions (PSFs), which are fixed and only need to be recorded once for all time use, are employed to recover corresponding spatial regions of an object by deconvolution algorithm. Then an automatic weighted averaging in an iterative process is performed to obtain the object with significantly enlarged FOV. Our results present an important step toward an advanced imaging technique with strongly scattering media.
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PDF链接：
https://arxiv.org/pdf/1707.09577