| Title | High-Speed Hyperspectral Video Acquisition By Combining Nyquist and Compressive Sampling |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Wang, Lizhi, Xiong, Zhiwei, Huang, Hua, Shi, Guangming, Wu, Feng, Zeng, Wenjun |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 41 |
| Pagination | 857–870 |
| ISSN | 1939-3539 |
| Keywords | 4D high-speed hyperspectral videos, Apertures, band-wise video, Cameras, complementary sampling, composability, compressive sampling, computational reconstruction, Cyber-physical systems, geophysical image processing, high light throughput, high spatial resolution, high-frame-rate panchromatic video, hybrid imaging, Hyperspectral imaging, hyperspectral video, Image reconstruction, image sensors, Imaging, inherent structural similarity, learning (artificial intelligence), low-frame-rate hyperspectral video, Nyquist sampling, privacy, pubcrawl, Resiliency, signal sampling, simultaneous sparsity, simultaneous spectral sparse model, Spatial resolution, spectral dimension, spectral resolution, speed hyperspectral video acquisition, temporal dimension, underlying HSHS video, video signal processing |
| Abstract | We propose a novel hybrid imaging system to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. The proposed system consists of two branches: one branch performs Nyquist sampling in the temporal dimension while integrating the whole spectrum, resulting in a high-frame-rate panchromatic video; the other branch performs compressive sampling in the spectral dimension with longer exposures, resulting in a low-frame-rate hyperspectral video. Owing to the high light throughput and complementary sampling, these two branches jointly provide reliable measurements for recovering the underlying HSHS video. Moreover, the panchromatic video can be used to learn an over-complete 3D dictionary to represent each band-wise video sparsely, thanks to the inherent structural similarity in the spectral dimension. Based on the joint measurements and the self-adaptive dictionary, we further propose a simultaneous spectral sparse (3S) model to reinforce the structural similarity across different bands and develop an efficient computational reconstruction algorithm to recover the HSHS video. Both simulation and hardware experiments validate the effectiveness of the proposed approach. To the best of our knowledge, this is the first time that hyperspectral videos can be acquired at a frame rate up to 100fps with commodity optical elements and under ordinary indoor illumination. |
| DOI | 10.1109/TPAMI.2018.2817496 |
| Citation Key | wang_high-speed_2019 |
High-Speed Hyperspectral Video Acquisition By Combining Nyquist and Compressive Sampling