PYI: Statistical Techniques in Inverse Problems

Bresler这项研究分为四个大的领域：图像重建、时变分布重建、传感器阵列处理和多参数数据可视化。在图像处理领域，主要目标是发展利用统计模型从部分和噪声数据中重建超分辨率图像的理论和相关的计算算法。对于第二个领域，目标是开发受物理或经济限制的最佳信号采集方案，以及相关的高效重建算法，用于成像采集过程中随时间变化的空间数据。在传感器区域处理领域，正在解决几个问题，包括为模型拟合问题的(次)最优解、宽带源定位和传感器阵列成像的计算高效算法的设计。最后，在最后一个领域，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式遥感和诊断成像中获取的数据。

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.