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摘要翻译：
在传统的光学成像系统中，空间分辨率受到衍射物理的限制，衍射物理起着低通滤波器的作用。关于亚波长特征的信息是由消逝波携带的，永远不会到达相机，因此对分辨率提出了一个严格的限制：所谓的衍射极限。现代显微方法通过使用花期技术实现了超分辨率。最先进的基于定位的荧光亚波长成像技术，如PALM和STORM实现了几十纳米米的亚衍射空间分辨率。然而，它们需要数万次曝光，这限制了它们的时间分辨率。我们最近提出了SPARCOM（基于稀疏性的超分辨率相关显微镜），它利用了荧光团分布的稀疏性，以及不相关发射的统计先验，并表明SPARCOM实现了与PALM/STORM相当的空间分辨率，同时捕获数据的速度是PALM/STORM的数百倍。这里，我们提供了一个详细的SPARCOM的数学公式，这反过来导致了一个有效的数值实现，适用于大规模问题。我们进一步将我们的方法扩展到基于稀疏性的超分辨率成像的一般框架，其中稀疏性可以假设在其他领域，如小波或离散余弦，导致在各种物理环境下改进的重建。
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英文标题：
《SPARCOM: Sparsity Based Super-Resolution Correlation Microscopy》
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作者：
Oren Solomon, Yonina C. Eldar, Maor Mutzafi and Mordechai Segev
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最新提交年份：
2018
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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：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：
In traditional optical imaging systems, the spatial resolution is limited by the physics of diffraction, which acts as a low-pass filter. The information on sub-wavelength features is carried by evanescent waves, never reaching the camera, thereby posing a hard limit on resolution: the so-called diffraction limit. Modern microscopic methods enable super-resolution, by employing florescence techniques. State-of-the-art localization based fluorescence subwavelength imaging techniques such as PALM and STORM achieve sub-diffraction spatial resolution of several tens of nano-meters. However, they require tens of thousands of exposures, which limits their temporal resolution. We have recently proposed SPARCOM (sparsity based super-resolution correlation microscopy), which exploits the sparse nature of the fluorophores distribution, alongside a statistical prior of uncorrelated emissions, and showed that SPARCOM achieves spatial resolution comparable to PALM/STORM, while capturing the data hundreds of times faster. Here, we provide a detailed mathematical formulation of SPARCOM, which in turn leads to an efficient numerical implementation, suitable for large-scale problems. We further extend our method to a general framework for sparsity based super-resolution imaging, in which sparsity can be assumed in other domains such as wavelet or discrete-cosine, leading to improved reconstructions in a variety of physical settings.
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PDF链接：
https://arxiv.org/pdf/1707.09255
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