SOTAVerified

Compressive Sensing

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 597 papers

TitleStatusHype
Operational Support Estimator NetworksCode0
Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing—0
Dynamic Path-Controllable Deep Unfolding Network for Compressive SensingCode1
MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction—0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels—0
Recursions Are All You Need: Towards Efficient Deep Unfolding NetworksCode0
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing—0
Optimization-Inspired Cross-Attention Transformer for Compressive SensingCode1
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Compressive Sensing with Tensorized Autoencoder—0
Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas—0
STAR-RIS-Enabled Simultaneous Indoor and Outdoor 3D Localization: Theoretical Analysis and Algorithmic Design—0
Compressed domain vibration detection and classification for distributed acoustic sensing—0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing—0
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography—0
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks—0
Signal processing with optical quadratic random sketches—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
Graph-based Semi-supervised Local Clustering with Few Labeled NodesCode0
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares—0
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
Show:102550
← PrevPage 4 of 24Next →
All datasetsSet11 cs=50%BSD68 CS=50%BSDS100 - 2x upscalingUrban100 - 2x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMP-DUN-Plus (4-step)Average PSNR42.82—Unverified
2AMPA-NetAverage PSNR40.32—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.86—Unverified