Practical Compressed Sensing

实用压缩感知

• 批准号: 0635234
• 负责人: Yoram Bresler
• 金额: $ 53.09万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0635234&HistoricalAwards=false
• 关键词: Practical; Compressed; Sensing

Sampling, or the conversion of continuous-domain real-life signals to discrete numbers that can be manipulated by computers, is the essential bridge between the analog and the digital world. Our entire digital information revolution relies on this fundamental process. However, in a wide range of sensing and sampling problems, due to physical constraints or timing requirements only a limited number of measurements can be acquired from the unknown object. A recent breakthrough in mathematics under the name compressed sensing shows that sparse or compressible finite length discrete signals can be recovered from small number of linear, non-adaptive (i.e., universal), and random measurements. This is important, because many signals of interest, including natural images, diagnostic images, videos, speech, music are sparse when represented in an appropriately chosen dictionary. This research extends the current methods in compressed sensing to other setups that have overwhelming practical significance. These extensions include constrained acquisition, additional statistical prior on sparse signals, and infinite dimensional cases. The specific goals of this project are to: (1) improve signal reconstruction quality; (2) reduce number of measurements required to achieve a specified reconstruction quality; (3) speed up the reconstruction time; and (4) to demonstrate these gains on real applications, and in particular in challenging magnetic resonance imaging applications, including functional imaging of the human brain. These goals are achieved by effectively exploiting additional priors about unknown signals to design optimized acquisition/sensing schemes that facilitate faster and more accurate reconstruction. The project develops a series of novel algorithms for optimized acquisition design and signal reconstruction. The performance of these methods is theoretically characterized and evaluated in real data and applications.

采样，或将连续域的真实信号转换为可由计算机操纵的离散数字，是模拟和数字世界之间的重要桥梁。 我们的整个数字信息革命都依赖于这个基本过程。 然而，在广泛的传感和采样问题，由于物理约束或定时要求，只有有限数量的测量可以从未知的对象。 最近在数学中以压缩感测的名义取得的突破表明，稀疏或可压缩的有限长度离散信号可以从少量的线性、非自适应（即，通用）和随机测量。 这很重要，因为许多感兴趣的信号，包括自然图像、诊断图像、视频、语音、音乐，当在适当选择的字典中表示时是稀疏的。本研究将压缩感知中的现有方法扩展到其他具有压倒性实际意义的环境中。 这些扩展包括约束采集、稀疏信号的额外统计先验和无限维情况。 该项目的具体目标是：（1）提高信号重建质量;（2）减少实现指定重建质量所需的测量次数;（3）加快重建时间;以及（4）在真实的应用中证明这些增益，特别是在具有挑战性的磁共振成像应用中，包括人脑的功能成像。 这些目标是通过有效地利用关于未知信号的额外先验来设计优化的采集/感测方案来实现的，这些方案有助于更快和更准确的重建。 该项目开发了一系列新颖的算法，用于优化采集设计和信号重构。 这些方法的性能在理论上的特点和评价在真实的数据和应用。