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 531–540 of 597 papers

TitleStatusHype
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction—0
Group-based Sparse Representation for Image RestorationCode0
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization—0
Structural Group Sparse Representation for Image Compressive Sensing Recovery—0
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images—0
One-bit compressive sensing with norm estimation—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Compressive Pattern Matching on Multispectral Data—0
Show:102550
← PrevPage 54 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