l-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement

Xin Miao, Xin Yuan, Yunchen Pu, Vassilis Athitsos; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 4059-4069

Abstract


We propose the l-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, l), 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 l-net. Results on both simulation and real datasets demonstrate the significant advantages of l-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, l-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.

Related Material


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[bibtex]
@InProceedings{Miao_2019_ICCV,
author = {Miao, Xin and Yuan, Xin and Pu, Yunchen and Athitsos, Vassilis},
title = {l-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}