Reconstruction of Sparse Multi-dimensional Imaging Data for Time-efficient Imaging

稀疏多维成像数据的重建用于时间效率成像*在我目前由NSERC资助的研究计划的继续中，我计划进一步研究从稀疏数据重建图像的新技术。我的研究将集中在最近制定的一类方法上，即所谓的压缩传感方法。这些方法与数码相机中通常使用的图像压缩有一些共同的特性，以使照片更小。在压缩感知中，我们假设我们的最终图像可以由所需数据的子集形成。压缩感知描述了对有限的采集数据进行“解压缩”以形成高质量图像的过程。我的计划在未来五年的总体目标是探索多维数据的重建，将我们的工作从只沿两个或三个空间维度的稀疏抽样扩展到包括三个空间维度加时间的多维抽样。为了实现这一目标，我们还将改进方法，以改进1)图像重建方法和2)从压缩数据重建图像的客观评估。NSERC的支持将为该基础图像科学研究计划提供基础支持，并将主要支持该计划聘用的学员。*

Reconstruction of Sparse Multi-dimensional Imaging Data for Time-efficient Imaging****In a continuation of my current NSERC-funded research program, I plan to further investigate new techniques for the reconstruction of images from sparse data. My investigations will focus on a general class of recently formulated methods, known as compressed sensing approaches. These approaches share some properties with image compression commonly employed in digital cameras to make pictures smaller. In compressed sensing, we assume that our final images can be formed form a subset of the needed data. Compressed sensing describes the process at which the limited acquired data is "uncompressed" to form high quality images. The overarching objective of my program over the next five years is to explore the reconstruction of multi-dimensional data, by extending our work from sparse sampling only along two or three spatial dimensions, to multi-dimensional sampling including the three spatial dimensions plus time. In accomplishing this goal, we will also enhance methods for improving 1) image reconstruction approaches and 2) the objective assessment of image reconstructed from compressed data. NSERC support will provide foundational support for this program of fundamental image science research and will principally support trainees engaged by this program.******