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.

中文翻译：

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快照压缩成像的等级最小化。

快照压缩成像（SCI）是指将多个帧映射到单个测量中的压缩成像系统，其中视频压缩成像和高光谱压缩成像是两个代表性的应用。尽管已经证明了高速视频和高光谱图像的令人兴奋的结果，但不良的重建质量使SCI无法广泛应用。本文旨在通过利用所需信号中的高维结构来提高SCI的重建质量。我们建立了一个联合模型，以将视频/高光谱帧的非局部自相似性以及秩最小化方法与SCI感测过程相集成。此后，开发了一种交替的最小化算法来解决此非凸问题。我们将进一步研究SCI中采样过程的特殊结构，以解决SCI重建中的计算量和内存问题。仿真和实际数据（由四个不同的SCI摄像机捕获）结果均表明，与当前的最新算法相比，我们提出的算法带来了显着的改进。我们希望我们的结果将鼓励研究人员和工程师进一步为实际应用开发压缩成像。