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 281–290 of 597 papers

TitleStatusHype
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization—0
Group Sparse Coding with a Laplacian Scale Mixture Prior—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System—0
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging—0
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—0
High-Dimensional Confidence Regions in Sparse MRI—0
High SNR Consistent Compressive Sensing—0
Compressive Sensing Based Sparse MIMO Array Optimization for Wideband Near-Field Imaging—0
Show:102550
← PrevPage 29 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