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 376–400 of 597 papers

TitleStatusHype
In-sector Compressive Beam Alignment for MmWave and THz Radios—0
Instance Optimal Decoding and the Restricted Isometry Property—0
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation—0
IoT Connectivity Technologies and Applications: A Survey—0
ISAR imaging of space objects using encoded apertures—0
Iterative Sparse Identification of Nonlinear Dynamics—0
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels—0
Joint group and residual sparse coding for image compressive sensing—0
Joint Localization and Information Transfer for Reconfigurable Intelligent Surface Aided Full-Duplex Systems—0
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Joint optimization for compressive video sensing and reconstruction under hardware constraints—0
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing—0
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
Kinetic Compressive Sensing—0
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems—0
Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimization—0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT—0
Lensless Imaging by Compressive Sensing—0
Lensless Imaging with Compressive Ultrafast Sensing—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework—0
Show:102550
← PrevPage 16 of 24Next →
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