PYI: Statistical Techniques in Inverse Problems

PYI：反问题中的统计技术

• 批准号: 9157377
• 负责人: Yoram Bresler
• 金额: $ 30万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1998-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9157377&HistoricalAwards=false
• 关键词: PYI; Statistical; Techniques; Inverse; Problems

Bresler This research falls into four broad areas: image reconstruction, reconstruction of time-varying distributions, sensor array processing, and visualization of multiparameter data. In the area of image processing, the principal objective is to develop the theory and associated computational algorithms for superresolution image reconstruction from partial and noisy data, using statistical models. For the second area, the goal is to develop optimum signal acquisition schemes subject to physical or economic constraints, and the associated efficient reconstruction algorithms, for imaging spatial data that is time varying during the acquisition process. In the area of sensor area processing, several issues are being addressed including the design of computationally efficient algorithms for the (sub)optimal solutions of model fitting problems, wideband source location, and imaging with sensor arrays. Finally, in the last area, the goal is to address the effective fusion, display, and visualization of multi-parameter spatially-related data, such as is acquired in multispectral, or multi-modality remote sensing and diagnostic imaging.

布雷斯勒这项研究分为四个广泛的领域：图像重建，时间变化分布的重建，传感器阵列处理以及多参数数据的可视化。在图像处理领域，主要目标是使用统计模型开发从部分和嘈杂数据的超分辨率图像重建的理论和相关计算算法。对于第二个领域，目标是制定受物理或经济限制的最佳信号获取方案以及相关的有效重建算法，以使在获取过程中时间变化的空间数据成像。在传感器区域处理的区域中，正在解决一些问题，包括设计模型拟合问题，宽带源位置和使用传感器阵列成像的（子）最佳解决方案的计算有效算法的设计。最后，在最后一个区域，目标是解决多参数与空间相关数据的有效融合，显示和可视化，例如在多光谱或多模式遥感和诊断成像中获取。