Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging

• URL: http://arxiv.org/abs/2003.13654v2
• Date: Fri, 17 Jul 2020 21:10:04 GMT
• Title: Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
• Authors: Xin Yuan, Yang Liu, Jinli Suo and Qionghai Dai
• Abstract summary: Snapshot imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot. Applying SCI to deep-scale (HD or UHD) videos in our daily lives is still challenging. This paper develops fast flexible algorithms for SCI based on the plug-play framework.
• Score: 43.50482493611073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages of low-bandwidth, low-power and low-cost, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging. The bottleneck lies in the reconstruction algorithms; they are either too slow (iterative optimization algorithms) or not flexible to the encoding process (deep learning based end-to-end networks). In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework. In addition to the widely used PnP-ADMM method, we further propose the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload and prove the convergence of PnP-GAP under the SCI hardware constraints. By employing deep denoising priors, we first time show that PnP can recover a UHD color video ($3840\times 1644\times 48$ with PNSR above 30dB) from a snapshot 2D measurement. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm. The code is available at https://github.com/liuyang12/PnP-SCI.

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