CIF: Small: Blind Perfect Signal Reconstruction in Subsampled Multi-Channel Systems

CIF：小：子采样多通道系统中的盲完美信号重建

• 批准号: 1018789
• 负责人: Yoram Bresler
• 金额: $ 47.52万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018789&HistoricalAwards=false
• 关键词: CIF; Small; Blind; Perfect; Signal

Multi-channel systems arise whenever a signal is picked up in or sent to more than one location. They are ubiquitous in imaging and sensing, data communication and storage, and in all audio, speech, or image processing digital technologies. Their applications range from ultrasound or MRI diagnostic scanners, to radio astronomy. The rich theory of multi-channel systems addresses two scenarios: when the entire system, including the channels, can be freely designed; or when the channels may be fixed and unknown, but data at the output of each channel is fully acquired. However, in many important applications this is not the case. The characteristics of the channels are often dictated by the physics governing the sensing process, which are unknown because of interaction of the sensors with the environment or their miss-calibration. Furthermore, because of physical or cost constraints only partial (subsampled) channel data is available. The goal of this project is to develop, evaluate and demonstrate the fundamental theory and design tools to address this important class of problems.This project aims to extend the methods of blind multi-channel deconvolution, which are currently limited to non-subsampled systems , to provide blind perfect signal reconstruction from subsampled data. The specific aims of this project are to: (1) develop the theory of blind identification of subsampled multi-channel systems; (2) propose practical methods for blind identification of such systems using perfect (or near perfect) reconstruction filter banks; (3) provide analytical tools to study and quantify various tradeoffs between conditioning (noise performance), signal distortion, robustness, and computational cost; and (4) to demonstrate performance gains on real applications, and in particular in highly-accelerated multi-channel magnetic resonance imaging (MRI), and robust image super-resolution.

当信号在多个位置被拾取或发送到多个位置时，就会出现多通道系统。它们在成像和传感、数据通信和存储以及所有音频、语音或图像处理数字技术中无处不在。它们的应用范围从超声波或 MRI 诊断扫描仪到射电天文学。多通道系统丰富的理论解决了两种情况：整个系统，包括通道，可以自由设计；或者当通道可能是固定且未知的，但每个通道的输出处的数据被完全采集时。然而，在许多重要的应用中，情况并非如此。通道的特性通常由控制传感过程的物理特性决定，但由于传感器与环境的相互作用或其错误校准，这些特性是未知的。此外，由于物理或成本限制，仅部分（二次采样）通道数据可用。该项目的目标是开发、评估和演示解决此类重要问题的基础理论和设计工具。该项目旨在扩展目前仅限于非下采样系统的盲多通道反卷积方法，从子采样数据提供盲完美信号重建。 本项目的具体目标是：（1）发展子采样多通道系统盲识别理论； (2) 提出使用完美（或接近完美）重构滤波器组对此类系统进行盲识别的实用方法； (3) 提供分析工具来研究和量化调节（噪声性能）、信号失真、鲁棒性和计算成本之间的各种权衡； (4) 展示实际应用中的性能提升，特别是在高加速多通道磁共振成像 (MRI) 和强大的图像超分辨率方面。