Video Compressive Sensing
9 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Video Compressive Sensing
Most implemented papers
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing
This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS).
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
xyvirtualgroup/MetaSCI-CVPR2021
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CVPR 2021
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.
Memory-Efficient Network for Large-scale Video Compressive Sensing
BoChenGroup/RevSCI-net
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CVPR 2021
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.
LiSens --- A Scalable Architecture for Video Compressive Sensing
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.
Deep Fully-Connected Networks for Video Compressive Sensing
In this work we present a deep learning framework for video compressive sensing.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
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.
Towards Real-time Video Compressive Sensing on Mobile Devices
mcao92/mobilesci
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The fast evolving mobile devices and existing high-performance video SCI reconstruction algorithms motivate us to develop mobile reconstruction methods for real-world applications.
