Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging
Xin Yuan, Yang Liu, Jinli Suo, Qionghai Dai
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 global 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 (3840x1644x48 with PNSR above 30dB) from a snapshot 2D measurement. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm.
BibTeX
@inproceedings{cvpr2020_plugandplayalgor,
title = {Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging},
author = {Xin Yuan and Yang Liu and Jinli Suo and Qionghai Dai},
booktitle = {CVPR 2020},
year = {2020}
}