Collaborative Research: A Modern Autofocus Methodology with Applications to Radar Imaging

协作研究：现代自动对焦方法及其在雷达成像中的应用

• 批准号: 0430877
• 负责人: Minh Do
• 金额: $ 17.53万
• 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: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430877&HistoricalAwards=false
• 关键词: Collaborative; Research; Modern; Autofocus; Methodology

Synthetic aperture radar (SAR), geophysical and seismic imaging, ultrasound imaging, optical coherence tomography (OCT), and many forms of astronomical imaging are examples of coherent imaging systems. In practice, the acquired phase data are noisy due to imprecise knowledge of the motion of the sensor, or due to the properties of the medium. As a consequence, the produced imagery is improperly focused. Existing techniques for correcting the imagery, referred to as autofocus, are ill-equipped to handle the challenges of modern high-resolution systems. The availability of well-focused ultra-high-resolution SAR imagery is crucial for antiterrorism efforts. Improved focusing may also lead to breakthroughs in medical imaging, such as ultrasound imaging of the human breast.The investigators develop a new generation of autofocus algorithms that will enable improved focusing in a variety of coherent imaging modalities. Primarily, the SAR autofocus problem is considered because of its national importance in all-weather surveillance, and also because the SAR field is highly developed. The research will focus on the following approaches: (i) optimization strategies that optimize image sharpness metrics, (ii) multiresolution vector space methods that exploit the structure of the phase error, and (iii) multichannel blind deconvolution techniques that exploit the redundancy of the blurring operation on each row of the image. These approaches motivate the development of a unified framework, in which all of the available assumptions are used in a robust restoration procedure. The implementation of the methodology in practical SAR systems will be emphasized. Finally, the developed autofocus methodology will be extended and applied to ultrasound, OCT, and passive radar imaging.

合成孔径雷达（SAR）、地球物理和地震成像、超声成像、光学相干断层扫描（OCT）以及许多形式的天文成像都是相干成像系统的示例。 在实践中，由于对传感器的运动的不精确的了解，或者由于介质的性质，所采集的相位数据是有噪声的。 因此，所产生的图像聚焦不当。 用于校正图像的现有技术（称为自动对焦）不足以应对现代高分辨率系统的挑战。 聚焦良好的超高分辨率合成孔径雷达图像的可用性对反恐工作至关重要。聚焦的改进也可能导致医学成像的突破，例如人类乳房的超声成像。研究人员开发了新一代的自动聚焦算法，这将使各种相干成像模式的聚焦得以改进。首先，合成孔径雷达的自聚焦问题被认为是因为它在全天候监视国家的重要性，也因为合成孔径雷达领域的高度发达。 研究将集中在以下方法：（i）优化图像清晰度指标的优化策略，（ii）利用相位误差结构的多分辨率向量空间方法，以及（iii）利用图像每行模糊操作冗余的多通道盲解卷积技术。这些方法促进了统一框架的发展，在该框架中，所有可用的假设都用于鲁棒恢复过程。 将强调在实际的SAR系统的方法的实施。 最后，所开发的自动聚焦方法将被扩展并应用于超声、OCT和无源雷达成像。