Video Compressive Sensing
8 papers with code • 0 benchmarks • 0 datasets
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This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS).
To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
The measurement rate of cameras that take spatially multiplexed measurements by using spatial light modulators (SLM) is often limited by the switching speed of the SLMs.
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing
We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure.