Algorithms and Software for High-Resolution Imaging

高分辨率成像的算法和软件

• 批准号: 9732070
• 负责人: Robert Plemmons
• 金额: --
• 依托单位: Wake Forest University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9732070&HistoricalAwards=false
• 关键词: Algorithms; Software; Resolution; Imaging

High-resolution images are essential in many important areas of science, engineering, law enforcement, and medicine. The need to extract meaningful information from degraded images is vital in many areas such as aero-optics imaging, surveillance photography, confocal microscopy, and medical imaging. Sources of image degradation vary among application areas, but include atmospheric turbulence, turbidity in a fluid medium, motion blur, and numerous other effects. Image processing tasks such as image enhancement, reconstruction, and restoration are computationally intensive; the underlying physical models require the solution to data-massive and often ill-posed inverse problems where the solution may not depend continuously on the data. All such problems require significant research into the algorithm- architecture-application interaction and can profit substantially from the availability of high-performance software systems. Iterative solvers that exploit both the structure of the problem, and the parallel computing platform, appear to be the most viable candidates for diverse, large scale image enhancement problems. This research project includes the following technical topics: a) New iterative algorithms with effective regularization strategies for blind deconvolution image restoration using adaptive filtering and multichannel phase diversity methods. b) Acceleration of the iterative algorithms utilizing Krylov subspace based multi-level preconditioning. c) New algorithms which partially mitigate the ill-posed nature of the image restoration problem by integrating numerical and symbolic computational methods. d) Development of a high performance software package, entitled "Parallel Image Processing Environment (pipe})", which utilizes the image processing algorithms developed in the course of this research.

高分辨率图像在科学、工程、执法和医学的许多重要领域中都是必不可少的。从退化的图像中提取有意义的信息在许多领域都是至关重要的，例如气动光学成像、监视摄影、共焦显微镜和医学成像。图像降级的来源因应用领域的不同而不同，但包括大气湍流、流体介质中的浑浊、运动模糊和许多其他效果。图像增强、重建和恢复等图像处理任务是计算密集型的；底层物理模型需要解决数据-海量且通常不适定的逆问题，其中解决方案可能不连续依赖于数据。所有这些问题都需要对算法-体系结构-应用程序交互进行大量研究，并且可以从高性能软件系统的可用性中获益良多。迭代求解器既利用了问题的结构，又利用了并行计算平台，似乎是解决各种大规模图像增强问题最可行的候选方案。A)基于自适应滤波和多通道相位分集的盲反卷积图像恢复算法。B)利用基于Krylov子空间的多层预条件加速迭代算法。C)通过将数值计算方法和符号计算方法相结合来部分缓解图像恢复问题的不适定性的新算法。D)开发一个高性能的软件包，题为“并行图像处理环境(PIPE)”，它利用在本研究过程中开发的图像处理算法。