4.7 Article Proceedings Paper

Compact Single-Shot Hyperspectral Imaging Using a Prism

期刊

ACM TRANSACTIONS ON GRAPHICS
卷 36, 期 6, 页码 -

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3130800.3130896

关键词

Hyperspectral imaging; image reconstruction

资金

  1. Korea NRF [2016R1A2B2013031, 2013M3A6A6073718]
  2. Korea Creative Content Agency (KOCCA) in Ministry of Culture, Cross-Ministry Giga KOREA Project [GK17P0200]
  3. Sports and Tourism (MCST), Samsung Electronics [SRFC-IT1402-02]
  4. ICT R&D program of MSIT/IITP of Korea [R7116-16-1035]
  5. European Research Council (ERC) under the European Union's Horizon research and innovation programme (CHAMELEON project) [682080]
  6. Spanish Ministerio de Economia y Competitividad [TIN2016-78753-P]
  7. Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [GK17P0200] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  8. National Research Foundation of Korea [2013M3A6A6073718, 2016R1A2B2013031] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

We present a novel, compact single-shot hyperspectral imaging method. It enables capturing hyperspectral images using a conventional DSLR camera equipped with just an ordinary refractive prism in front of the camera lens. Our computational imaging method reconstructs the full spectral information of a scene from dispersion over edges. Our setup requires no coded aperture mask, no slit, and no collimating optics, which are necessary for traditional hyperspectral imaging systems. It is thus very cost-effective, while still highly accurate. We tackle two main problems: First, since we do not rely on collimation, the sensor records a projection of the dispersion information, distorted by perspective. Second, available spectral cues are sparse, present only around object edges. We formulate an image formation model that can predict the perspective projection of dispersion, and a reconstruction method that can estimate the full spectral information of a scene from sparse dispersion information. Our results show that our method compares well with other state-of-the-art hyperspectral imaging systems, both in terms of spectral accuracy and spatial resolution, while being orders of magnitude cheaper than commercial imaging systems.

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