快照(计算机存储)
高光谱成像
计算机科学
迭代重建
人工智能
计算机视觉
带宽(计算)
算法
电信
操作系统
作者
Xin Miao,Xin Yuan,Yunchen Pu,Vassilis Athitsos
标识
DOI:10.1109/iccv.2019.00416
摘要
We propose the λ-net, which reconstructs hyperspectral images (e.g., with 24 spectral channels) from a single shot measurement. This task is usually termed snapshot compressive-spectral imaging (SCI), which enjoys low cost, low bandwidth and high-speed sensing rate via capturing the three-dimensional (3D) signal i.e., (x, y, λ), using a 2D snapshot. Though proposed more than a decade ago, the poor quality and low-speed of reconstruction algorithms preclude wide applications of SCI. To address this challenge, in this paper, we develop a dual-stage generative model to reconstruct the desired 3D signal in SCI, dubbed λ-net. Results on both simulation and real datasets demonstrate the significant advantages of λ-net, which leads to >4dB improvement in PSNR for real-mask-in-the-loop simulation data compared to the current state-of-the-art. Furthermore, λ-net can finish the reconstruction task within sub-seconds instead of hours taken by the most recently proposed DeSCI algorithm, thus speeding up the reconstruction >1000 times.
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