Compressive Sensing
109 papers with code • 5 benchmarks • 4 datasets
Compressive Sensing is a new signal processing framework for efficiently acquiring and reconstructing a signal that have a sparse representation in a fixed linear basis.
Source: Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
Benchmarks
These leaderboards are used to track progress in Compressive Sensing
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Libraries
Use these libraries to find Compressive Sensing models and implementations
BSD
Urban100
Set5
Set11
Most implemented papers
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices
Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.
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.
Generalization Bounds for Sparse Random Feature Expansions
In particular, we provide generalization bounds for functions in a certain class (that is dense in a reproducing kernel Hilbert space) depending on the number of samples and the distribution of features.
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
caiyuanhao1998/MST
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CVPR 2022
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
Compressed sensing enables the reconstruction of high-resolution signals from under-sampled data.
Group-based Sparse Representation for Image Restoration
In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR).
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.
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
Tucker decomposition is the cornerstone of modern machine learning on tensorial data analysis, which have attracted considerable attention for multiway feature extraction, compressive sensing, and tensor completion.
