Various Total Variation for Snapshot Video Compressive Imaging
This work addresses the challenge of efficiently reconstructing 3D video from 2D sensor data in snapshot compressive imaging, but it is incremental as it compares existing TV variants rather than introducing a new method.
The paper investigates which total variation penalty—anisotropic, isotropic, or vectorized TV—is most effective for reconstructing high-dimensional video from snapshot compressive imaging, finding that TV-based methods offer a good trade-off between speed and performance.
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.