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首页> 外文会议>Computational Imaging V; Proceedings of SPIE-The International Society for Optical Engineering; vol.6498; Electronic Imaging Science and Technology >Multiscale reconstruction for computational spectral imaging
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Multiscale reconstruction for computational spectral imaging

机译：用于计算光谱成像的多尺度重建

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In this work we develop a spectral imaging system and associated reconstruction methods that have been designed to exploit the theory of compressive sensing. Recent work in this emerging field indicates that when the signal of interest is very sparse (i.e. zero-valued at most locations) or highly compressible in some basis, relatively few incoherent observations are necessary to reconstruct the most significant non-zero signal components. Conventionally, spectral imaging systems measure complete data cubes and are subject to performance limiting tradeoffs between spectral and spatial resolution. We achieve single-shot full 3D data cube estimates by using compressed sensing reconstruction methods to process observations collected using an innovative, real-time, dual-disperser spectral imager. The physical system contains a transmissive coding element located between a pair of matched dispersers, so that each pixel measurement is the coded projection of the spectrum in the corresponding spatial location in the spectral data cube. Using a novel multiscale representation of the spectral image data cube, we are able to accurately reconstruct 256 × 256 × 15 spectral image cubes using just 256 × 256 measurements.
机译：在这项工作中，我们开发了一种光谱成像系统和相关的重建方法，这些系统旨在利用压缩感测理论进行设计。在这个新兴领域的最新工作表明，当感兴趣的信号非常稀疏（即大多数位置为零值）或某种程度上高度可压缩时，重构最重要的非零信号分量所需的相干观测就相对较少。常规地，光谱成像系统测量完整的数据立方体，并且受到光谱和空间分辨率之间性能限制的折衷。通过使用压缩的传感重建方法来处理使用创新的实时双色散光谱成像仪收集的观测值，我们可以实现单次全3D数据立方体估计。物理系统包含位于一对匹配的分散器之间的透射编码元素，因此每个像素测量值是光谱数据立方体中相应空间位置中光谱的编码投影。使用新颖的光谱图像数据立方体的多尺度表示，我们仅使用256×256次测量就能够准确地重建256×256×15个光谱图像立方体。

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