摘要翻译：
在基于扫描显微镜的成像技术中，需要开发新的数据采集方案，以减少数据采集时间，并最大限度地减少样品暴露在探测辐射中。稀疏采样方案非常适合于这样的应用，其中图像可以从稀疏的测量集重建。特别是基于监督学习的动态稀疏采样在实际应用中显示出了很好的效果。然而，这种方法的一个特殊缺点是，它需要训练具有相似信息内容的图像集，而这些信息内容可能并不总是可用的。本文提出了一种基于深度神经网络的有监督学习动态采样(SLADS)算法。我们称这种算法为slads-net。我们利用SLADS-Net进行了动态采样的模拟实验，其中训练图像与测试图像具有相似的信息含量或完全不同的信息含量。我们比较了各种训练方法如最小二乘、支持向量回归和深度神经网络的性能。从这些结果中我们观察到，当训练图像和测试图像不相似时，基于深度神经网络的训练结果具有更好的性能。我们还讨论了一个预先训练的SLADS网络的开发，该网络使用通用图像进行训练。在此，对神经网络参数进行了预训练，以便用户可以直接应用SLADS-Net进行成像实验。
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英文标题：
《SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep
  Neural Networks》
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作者：
Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy,
  Charles A. Bouman, Charudatta Phatak
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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：
  In scanning microscopy based imaging techniques, there is a need to develop novel data acquisition schemes that can reduce the time for data acquisition and minimize sample exposure to the probing radiation. Sparse sampling schemes are ideally suited for such applications where the images can be reconstructed from a sparse set of measurements. In particular, dynamic sparse sampling based on supervised learning has shown promising results for practical applications. However, a particular drawback of such methods is that it requires training image sets with similar information content which may not always be available. In this paper, we introduce a Supervised Learning Approach for Dynamic Sampling (SLADS) algorithm that uses a deep neural network based training approach. We call this algorithm SLADS- Net. We have performed simulated experiments for dynamic sampling using SLADS-Net in which the training images either have similar information content or completely different information content, when compared to the testing images. We compare the performance across various methods for training such as least- squares, support vector regression and deep neural networks. From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar. We also discuss the development of a pre-trained SLADS-Net that uses generic images for training. Here, the neural network parameters are pre-trained so that users can directly apply SLADS-Net for imaging experiments.
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PDF链接：
https://arxiv.org/pdf/1803.02972