1 research outputs found
Rank Minimization for Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems
where multiple frames are mapped into a single measurement, with video
compressive imaging and hyperspectral compressive imaging as two representative
applications. Though exciting results of high-speed videos and hyperspectral
images have been demonstrated, the poor reconstruction quality precludes SCI
from wide applications.This paper aims to boost the reconstruction quality of
SCI via exploiting the high-dimensional structure in the desired signal. We
build a joint model to integrate the nonlocal self-similarity of
video/hyperspectral frames and the rank minimization approach with the SCI
sensing process. Following this, an alternating minimization algorithm is
developed to solve this non-convex problem. We further investigate the special
structure of the sampling process in SCI to tackle the computational workload
and memory issues in SCI reconstruction. Both simulation and real data
(captured by four different SCI cameras) results demonstrate that our proposed
algorithm leads to significant improvements compared with current
state-of-the-art algorithms. We hope our results will encourage the researchers
and engineers to pursue further in compressive imaging for real applications.Comment: 18 pages, 21 figures, and 2 tables. Code available at
https://github.com/liuyang12/DeSC