空间编码压缩光谱成像中空间光谱重构的实现与评价

Yuheng Chen, Jiankang Zhou, Xin-hua Chen, Yiqun Ji, Wei-min Shen
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引用次数: 0

摘要

压缩光谱成像将传统的光谱成像技术与压缩感知技术相结合,通过在机器视觉、生物医学成像等领域的应用,初步探索了压缩光谱成像技术的应用能力。本文提出了一种基于稀疏恢复的空间谱重建方法,并对其性能进行了估计。利用AVIRIS可见光波段数据进行模拟成像实验,构建基于两步迭代收缩/阈值算法的特定模块,对数据立方体进行重构。采用统计指标对数据保真度进行定量判断,从而明显体现正则化指标优化和帧数增加对提高重构数据保真度的效果。通过对恢复图像和光谱曲线的视觉判断,评价了不同地面物种的空间光谱特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation and evaluation of spatio-spectral reconstruction for spatial coding compressive spectral imaging
Compressive spectral imaging combines traditional spectral imaging technique with compressive sensing and its preliminary application ability has been early explored by the usage on the occasions such as machine-vision and biomedical imaging. In this paper, spatio-spectral reconstruction based on sparse recovery method is presented and its performance is estimated. Simulating imaging experiment is carried out by using AVIRIS visible band data and specific module based on two-step iterative shrinkage/thresholding algorithm is built so as to execute reconstruction for the data cube. Statistical index is adopted so as to judge data fidelity quantitatively so that the effect of the regularizer index optimization and frame shooting number increasing on the improvement of reconstructed data fidelity can be distinctly revealed. The spatio-spectral featuring of different ground species are also evaluated by visual judgment on restored images and spectral curves.
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