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 271–280 of 597 papers

TitleStatusHype
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
An Ensemble Approach for Compressive Sensing with Quantum—0
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
The Power of Triply Complementary Priors for Image Compressive Sensing—0
Provable Convergence of Plug-and-Play Priors with MMSE denoisers—0
Site-specific online compressive beam codebook learning in mmWave vehicular communication—0
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
Show:102550
← PrevPage 28 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