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- W4387092373 abstract "Multi-spectral and hyper-spectral imaging usually requires scanning in the spatial or spectral dimensions and thus the temporal resolution is often compromised. Although various methods have been proposed, it is still challenging to capture high spatial-temporal-spectral resolution simultaneously. In this paper, we propose LeSTI++, a <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>deep learning reconstruction</i> for an <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>LED-based compressive spectral-temporal imaging</i> system to overcome the scanning limitation of previous approaches. After describing the hardware setup, a three-step reconstruction algorithm is proposed, which is composed of a plug-and-play (PnP) algorithm, motion based frame interpolation, and spectra converter. In the first step, the captured measurement is decoded by a PnP framework using a trained spectral video (SpVi) denoiser, dubbed PnP-SpVi, which specially fits our data. Then in the second step, missing LED images are interpolated with motion estimation by a pre-trained neural network. Lastly, the predicted LED images are projected to spectral images by a trained converter. This algorithm allows us to capture high speed hyper-spectral targets and the reconstruction results achieve higher spectral and temporal resolutions simultaneously than previous solutions. We compare the proposed algorithm with state-of-the-art algorithms in simulation, and verify the proposed of LeSTI++ with experimental data. The code can be accessed at <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://github.com/dahaorendrm/SCI_2.0_python</uri> ." @default.
- W4387092373 created "2023-09-28" @default.
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- W4387092373 date "2023-01-01" @default.
- W4387092373 modified "2023-10-11" @default.
- W4387092373 title "High Resolution LED-based Snapshot Compressive Spectral Video Imaging with Deep Neural Networks" @default.
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- W4387092373 doi "https://doi.org/10.1109/tci.2023.3314969" @default.
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