Simultaneous Blind De-Convolution of Repeated Astronomical Exposures

在整个科学工作中，图像无处不在。虽然许多图片在它们的原始形式下是完全足够的，但在许多科学领域，单帧是不够的。改进这种结果以进行研究的传统方法包括组合多个帧，这通常将最终质量降低到大约最低公分母的质量，以及采用只从其中选择和组合最好的序列的长序列，从而丢弃大量信息。这是一个项目，旨在通过统计和图像处理寻找新的方法，将重复曝光结合起来，以产生质量更好、模糊更少、分辨率更高的图像，并通过自动管道处理来实现这一点。从所有精心收集的数据中保留最大信息量的高质量图像的价值是不可估量的，它将很好地影响到科学的各个领域，包括业余活动和公民科学项目，并且在任何图像模糊或模糊是限制因素的情况下通常都是有用的。如今，越来越大和越来越复杂的成像探测器是许多科学实验的主要数据来源。然而，它们产生的图像往往会因为快速变化的扭曲而变得模糊，比如天文物体和望远镜之间的大气。使用硬件消除此类影响非常复杂(例如，.自适应光学)或极其昂贵(例如，天基观测站)。要检测到较微弱的信号，需要多次曝光，传统上是通过卷积到最低可接受的质量来组合，但这样做会丢弃图像中的大量信息。一定有更好的办法。该项目将开发计算统计和图像处理的新方法，以实现重复曝光的最佳组合，以产生比原始图像具有最小模糊和更高分辨率的高质量图像。这就需要开发新的贝叶斯算法和工业强度可扩展的软件。这项研究将统计学、计算机科学和数据密集型科学结合到一个有针对性的、强大的研究计划中，应该会导致图像质量的重大飞跃。这一新想法可能会改变游戏规则，特别适合低信噪比图像，应该有助于设计新的实验和观察策略。开发的算法和工具将直接适用于气象学和基因组学等其他研究领域，并将是开放源代码和公开可用的。这些下一代数据挑战对于将承担大部分工作的研究生来说将是特别有价值的培训。

Images are ubiquitous throughout the scientific endeavor. Although many pictures are perfectly adequate in their original form, there are many scientific fields where single frames are not sufficient. Traditional methods of improving such results to enable research with them include combining multiple frames, which often reduces the final quality to around that of the lowest common denominator, and taking long sequences from which only the best are selected and combined, thus throwing away a lot of information. This is a project to find new ways through statistics and image processing to combine repeated exposures to produce images of superior quality, with less blur and higher resolution, and to carry this out with automatic pipeline processing. The value of high quality images that retain the maximum amount of information from all the painstakingly assembled data is inestimable, and will impact pretty well every field of science, including amateur activities and citizen science projects, and will be generally useful in any situation where blurred or faint pictures are a limiting factor.Imaging detectors of increasing size and complexity are nowadays the primary source of data in many scientific experiments. The images they produce, however, are often blurred by distortions that can change rapidly, such as the atmosphere between astronomical objects and telescopes. Eliminating such effects with hardware is either extremely complex (e.g,. adaptive optics) or extremely expensive (e.g., space-based observatories). To detect fainter signals requires multiple exposures, which are traditionally combined by convolving to the lowest acceptable quality, but doing that throws away a lot of the information in the images. There has to be a better way. This project will develop new methodologies in computational statistics and image processing for the optimal combination of repeated exposures to produce images of superior quality, having minimal blur and higher resolution than the originals. This requires developing novel Bayesian algorithms and industrial-strength scalable software.The study combines statistics, computer science and data-intensive science into a focused, powerful research program that should lead to a significant leap in image quality. The new idea is potentially game changing, is particularly well suited to low signal-to-noise images, and should help in the design of new experiments and observing strategies. The algorithms and tools developed will be directly applicable to other research fields as diverse as meteorology and genomics, and will be open-source and publicly available. These next-generation data challenges will be especially valuable training for the graduate student who will be doing much of the work.