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 401–425 of 597 papers

TitleStatusHype
Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing—0
Scalable Deep Compressive Sensing—0
Secure Directional Modulation with Movable Antenna Array Aided by RIS—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries—0
Signal processing after quadratic random sketching with optical units—0
Signal processing with optical quadratic random sketches—0
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)—0
Signal Recovery with Non-Expansive Generative Network Priors—0
Signal to Noise Ratio in Lensless Compressive Imaging—0
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing—0
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning—0
Single pixel imaging at high pixel resolutions—0
Site-specific online compressive beam codebook learning in mmWave vehicular communication—0
Sketching for Large-Scale Learning of Mixture Models—0
SnapCap: Efficient Snapshot Compressive Video Captioning—0
Snapshot fiber spectral imaging using speckle correlations and compressive sensing—0
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas—0
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—0
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior—0
Sparse Estimation with Structured Dictionaries—0
Sparse Models for Machine Learning—0
CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data—0
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—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