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 511–520 of 597 papers

TitleStatusHype
A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Ambient Occlusion via Compressive Visibility Estimation—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Analyzing the group sparsity based on the rank minimization methods—0
An Ensemble Approach for Compressive Sensing with Quantum—0
An Off-grid Compressive Sensing Strategy for the Subarray Synthesis of Non-uniform Linear Arrays—0
A Bayesian Lasso based Sparse Learning Model—0
Show:102550
← PrevPage 52 of 60Next →
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