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 201–225 of 597 papers

TitleStatusHype
Robust Symbol Detection in Overloaded NOMA Systems—0
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Distributed Compressive Sensing: A Deep Learning Approach—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
Distributed Video Adaptive Block Compressive Sensing—0
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
An Ensemble Approach for Compressive Sensing with Quantum—0
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling—0
Efficient Recovery of Jointly Sparse Vectors—0
Efficient Sampling for Learning Sparse Additive Models in High Dimensions—0
Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication—0
Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis—0
Energy-aware adaptive bi-Lipschitz embeddings—0
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—0
Error Resilient Deep Compressive Sensing—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Estimation with Low-Rank Time-Frequency Synthesis Models—0
Evaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation—0
Compressive Sensing MRI with Wavelet Tree Sparsity—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