PYI: Statistical Techniques in Inverse Problems

Bresler 这项研究福尔斯四个广泛的领域：图像重建，重建随时间变化的分布，传感器阵列处理，多参数数据的可视化。在图像处理领域，主要目标是开发理论和相关的计算算法，用于从部分和噪声数据中重建超分辨率图像，使用统计模型。对于第二个领域，我们的目标是开发最佳的信号采集方案的物理或经济的限制，以及相关的有效的重建算法，成像的空间数据是时变的采集过程中。在传感器区域处理领域，有几个问题正在得到解决，包括设计计算效率高的算法（次）模型拟合问题的最佳解决方案，宽带源定位和成像与传感器阵列。最后，在最后一个领域中，目标是解决多参数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态遥感和诊断成像中获得的数据。

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.