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 526–550 of 597 papers

TitleStatusHype
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit—0
Bayesian Compressive Sensing Using Normal Product Priors—0
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing—0
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks—0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—0
Blind Compressive Sensing Framework for Collaborative Filtering—0
Blind Orthogonal Least Squares based Compressive Spectrum Sensing—0
Block based Adaptive Compressive Sensing with Sampling Rate Control—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—0
Block-wise Lensless Compressive Camera—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach—0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—0
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications—0
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems—0
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture—0
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction—0
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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