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

重建稀疏多维成像数据以实现时间高效成像 * 在我目前的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。我的调查将集中在一般类最近制定的方法，被称为压缩传感方法。这些方法与数码相机中通常采用的图像压缩共享一些属性，以使图片更小。在压缩感知中，我们假设我们的最终图像可以形成所需数据的子集。压缩感知描述了有限的采集数据被“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，通过扩展我们的工作从稀疏采样只有沿着两个或三个空间维度，多维采样包括三个空间维度加上时间。在实现这一目标的过程中，我们还将增强用于改进1）图像重建方法和2）从压缩数据重建的图像的客观评估的方法。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.******