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
Latest papers with no code
A Compressive Sensing Based Method for Harmonic State Estimation
Power quality monitoring has become a vital need in modern power systems owing to the need for agile operation and troubleshooting scheme.
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems
The CSI feedback is reduced by 50%, and the memory reduction at both the UE and BS starts from 50% and increases with the number of subcarriers.
Signal processing after quadratic random sketching with optical units
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing
Video Compressed Sensing (VCS) aims to reconstruct multiple frames from one single captured measurement, thus achieving high-speed scene recording with a low-frame-rate sensor.
Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing
The performance of our proposed method is compared with benchmark methods, including compressive sensing (CS), BCS, and Laplacian-prior BCS (L-BCS).
MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction
To this end, we propose MOSAIC, a novel compressive sensing framework to reconstruct images given any random selection of measurements, sampled using a fixed basis.
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels
Block transmission systems have been proven successful over frequency-selective channels.
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
NL-CS Net is composed of the up-sampling module and the recovery module.
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications
Sparse structures are widely recognized and utilized in channel estimation.
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing
Deep network-based image and video Compressive Sensing(CS) has attracted increasing attentions in recent years.
