Simultaneous Blind De-Convolution of Repeated Astronomical Exposures

图像在科学奋进中无处不在。 虽然许多图片在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。 改进这些结果以使研究能够使用它们的传统方法包括组合多个帧，这通常会将最终质量降低到最小公分母的质量，并且只选择和组合最好的长序列，从而丢弃大量信息。 这是一个项目，通过统计和图像处理找到新的方法，联合收割机重复曝光，以产生上级质量的图像，具有更少的模糊和更高的分辨率，并通过自动流水线处理来实现这一点。 高质量图像的价值是不可估量的，它能从所有精心收集的数据中保留最大量的信息，并将很好地影响每一个科学领域，包括业余活动和公民科学项目，并且通常在模糊或微弱的图像是限制因素的任何情况下都是有用的。越来越大的尺寸和复杂性的成像探测器现在是许多科学研究中数据的主要来源。实验 然而，它们产生的图像往往会因快速变化的扭曲而模糊，例如天文物体和望远镜之间的大气层。 用硬件消除这种影响是极其复杂的（例如，自适应光学器件）或极其昂贵（例如，空间观测站）。 为了检测更微弱的信号，需要多次曝光，传统上通过卷积来组合到最低可接受的质量，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方面的新方法，以优化重复曝光的组合，产生上级质量的图像，具有最小的模糊和比原始图像更高的分辨率。 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重点突出、功能强大的研究项目中，应该会导致图像质量的重大飞跃。 这个新想法可能会改变游戏规则，特别适合低信噪比图像，并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，并且将是开源和公开的。 这些下一代数据的挑战将是特别有价值的培训研究生谁将做大部分的工作。

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.