Revisit Dictionary Learning for Video Compressive Sensing under the
Plug-and-Play Framework
- URL: http://arxiv.org/abs/2110.04966v1
- Date: Mon, 11 Oct 2021 02:30:54 GMT
- Title: Revisit Dictionary Learning for Video Compressive Sensing under the
Plug-and-Play Framework
- Authors: Qing Yang, Yaping Zhao
- Abstract summary: In this paper, we propose an efficient and effective compressive-based algorithm for video SCI reconstruction.
Our simulation results demonstrate the effectiveness of our proposed method.
- Score: 4.988065198958319
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Aiming at high-dimensional (HD) data acquisition and analysis, snapshot
compressive imaging (SCI) obtains the 2D compressed measurement of HD data with
optical imaging systems and reconstructs HD data using compressive sensing
algorithms. While the Plug-and-Play (PnP) framework offers an emerging solution
to SCI reconstruction, its intrinsic denoising process is still a challenging
problem. Unfortunately, existing denoisers in the PnP framework either suffer
limited performance or require extensive training data. In this paper, we
propose an efficient and effective shallow-learning-based algorithm for video
SCI reconstruction. Revisiting dictionary learning methods, we empower the PnP
framework with a new denoiser, the kernel singular value decomposition (KSVD).
Benefited from the advent of KSVD, our algorithm retains a good trade-off among
quality, speed, and training difficulty. On a variety of datasets, both
quantitative and qualitative evaluations of our simulation results demonstrate
the effectiveness of our proposed method. In comparison to a typical baseline
using total variation, our method achieves around $2$ dB improvement in PSNR
and 0.2 in SSIM. We expect that our proposed PnP-KSVD algorithm can serve as a
new baseline for video SCI reconstruction.
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