Mathematical Sciences: Statistical Methods in Image Processing

该项目涉及三个相关的问题， 图像处理的一般领域。 第一个是戏剧性的 吉布斯取样器和大都会的效率提高 动态算法从限制算法到局部 有界象空间 第二种是一种特殊的估计 一种用于暗盘的天文图像数据的方法， 数据具有显著的模糊（例如，哈勃望远镜数据）。 这里 适当的低维线性函数的使用改善了 效率第三个地址使用马尔可夫数据压缩 随机场模型和细分原始大图像， 形成最佳代表子图像。 统计图像分析使用统计原理， "猜测"真实的图像时，只有质量差的图像数据， available. 特定算法的质量取决于 选择的"猜测"有多好以及实现的速度有多快。 这个项目的重点是提高算法的速度 试图获取最相关的信息，而不是 一次完成整个图像

This project deals with three related problems in the general area of image processing. The first is a dramatic increase in the efficiency of the Gibbs Sampler and Metropolis Dynamic algorithms from restricting the algorithms to a locally bounded image space. The second develops a special estimation method for astronomical image data of a faint disk, where the data has significant blur (e.g., Hubble telescope data). Here the use of a suitable lower dimensional linear function improves efficiency. The third addresses data compression using a Markov random field model and subdividing the original large image and forming optimal representative subimages. Statistical image analysis uses statistical principles to "guess" the real image when only poor quality image data is available. The quality of a particular algorithm is depends upon how good the chosen "guess" is and how quickly it is achieved. This project focusses on increasing the speed of the algorithms by trying to get the most pertinent information rather than working on the whole image at once.