[电气工程与系统科学] 电容层析成像的一种重建算法 基于实验数据的全变分和L0-范数正则化 [推广有奖]

摘要翻译：
电容层析成像(ECT)以其非侵入性和低成本的优点在许多领域得到了广泛的研究。采用L1范数正则化的稀疏算法，如全变差正则化(TV)来减小平滑效应，获得清晰的图像。本文首次提出用L0范数正则化算法，即双外推近似迭代硬阈值(DEPIHT)算法求解ECT逆问题。采用基于TV正则化的加速交替方向乘法器(AADMM)算法获取DEPIHT算法的第一点。通过实验验证了AADMM-DEPIHT算法的可行性，并与Landweber迭代(LI)和AADMM算法进行了比较。实验结果表明，AADMM-DEPIHT算法对图像质量有较大的改善，同时也表明DEPIHT算法可以作为ECT后处理的一个合适的候选算法。
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英文标题：
《A reconstruction algorithm for electrical capacitance tomography via
  total variation and l0-norm regularizations using experimental data》
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作者：
Jiaoxuan Chen, Maomao Zhang and Yi Li
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最新提交年份：
2017
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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：
  Electrical capacitance tomography (ECT) has been investigated in many fields due to its advantages of being non-invasive and low cost. Sparse algorithms with l1-norm regularization are used to reduce the smoothing effect and obtain sharp images, such as total variation (TV)regularization. This paper proposed for the first time to solve the ECT inverse problem using an l0-norm regularization algorithm, namely the doubly extrapolated proximal iterative hard thresholding (DEPIHT) algorithm. The accelerated alternating direction method of multipliers (AADMM) algorithm, based on the TV regularization, has been selected to acquire the first point for the DEPIHT algorithm. Experimental tests were carried out to validate the feasibility of the AADMM-DEPIHT algorithm,which is compared with the Landweber iteration (LI) and AADMM algorithms. The results show the AADMM-DEPIHT algorithm has an improvement on the quality of images and also indicates that the DEPIHT algorithm can be a suitable candidate for ECT in post-process.
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PDF链接：
https://arxiv.org/pdf/1711.02544