Multidimensional optical compressive imaging Conference Paper uri icon

abstract

  • I. INTRODUCTION The theory of Compressive Sensing, aka Compressed Sampling (CS) is now roughly a decade old and it has generated a great deal of attention in a variety of areas. The CS theory offers a sensing framework that makes it possible to reconstruct signals from very few measurements. The CS theory has found natural applications in optics [1]. CS application for optical multidimensional sensing is particularly appealing because the typical high dimensionality of the data, its compressibility, and because sensing limitations due to the lack of hardware that can measure directly more than two dimensions … II. WHY APPLYING CS FOR MULTIDIMENSIONAL OPTICAL IMAGING? Multidimensional imaging [2], [3], aims to capture object properties beyond the two dimensional (2D) spatial distributions, such as depth, spectra, time, phase, polarization state, propagation direction, and coherence. With multi-dimension sensing, optical data …

publication date

  • July 3, 2017