论文标题
快照视频压缩成像的各种总变化
Various Total Variation for Snapshot Video Compressive Imaging
论文作者
论文摘要
由于传感器的可用性有限，对高维图像进行采样具有挑战性。在这些情况下，通常需要扫描。为了减轻这一挑战，提出了快照压缩成像（SCI），以使用2D传感器（检测器）捕获高维（通常为3D）图像。通过新颖的光学设计，传感器捕获的{\ em测量}是3D所需信号的多个帧的编码图像。此后，采用重建算法来检索高维数据。尽管已经提出了各种算法，但由于计算时间和性能之间的良好权衡，基于总变化（TV）的方法仍然是最有效的方法。本文旨在回答哪个电视罚款（各向异性电视，各向同性电视和矢量电视）的问题最适合视频SCI重建？在模拟和真实数据集上开发和测试了各种电视deNoising和投影算法，以进行视频SCI重建。
Sampling high-dimensional images is challenging due to limited availability of sensors; scanning is usually necessary in these cases. To mitigate this challenge, snapshot compressive imaging (SCI) was proposed to capture the high-dimensional (usually 3D) images using a 2D sensor (detector). Via novel optical design, the {\em measurement} captured by the sensor is an encoded image of multiple frames of the 3D desired signal. Following this, reconstruction algorithms are employed to retrieve the high-dimensional data. Though various algorithms have been proposed, the total variation (TV) based method is still the most efficient one due to a good trade-off between computational time and performance. This paper aims to answer the question of which TV penalty (anisotropic TV, isotropic TV and vectorized TV) works best for video SCI reconstruction? Various TV denoising and projection algorithms are developed and tested for video SCI reconstruction on both simulation and real datasets.
