Video Compressive Sensing
8 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.
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.
