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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.
We consider the reconstruction problem of video snapshot compressive imaging (SCI), which captures high-speed videos using a low-speed 2D sensor (detector). The underlying principle of SCI is to modulate sequential high-speed frames with different ma
We consider using {bfem untrained neural networks} to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Vari
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 proble
Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a two-dimensional (2D) detector to capture HD ($ge3$D) data in a {em snapshot} measurement. Via novel optic
Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we incorporate the interpretability of model-based methods and the speed of