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
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
xchen-tamu/linear-lista-cpss
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NeurIPS 2018
In this work, we study unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery.
Learning to compress and search visual data in large-scale systems
The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
caiyuanhao1998/MST
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CVPR 2022
The HSI representations are highly similar and correlated across the spectral dimension.
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).
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.
Provable Dynamic Robust PCA or Robust Subspace Tracking
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).
Multilinear Compressive Learning
viebboy/MultilinearCompressiveLearningFramework
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Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
GauriJagatap/invimaging-deeppriors
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NeurIPS 2019
Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval.
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput.
