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

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Showing 101–150 of 597 papers

TitleStatusHype
ISAR imaging of space objects using encoded apertures—0
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain—0
Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces—0
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing—0
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—0
Fast Disparity Estimation from a Single Compressed Light Field Measurement—0
Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service—0
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
Joint Localization and Information Transfer for Reconfigurable Intelligent Surface Aided Full-Duplex Systems—0
Compressive Sensing Based Sparse MIMO Array Optimization for Wideband Near-Field Imaging—0
A Spatially Separable Attention Mechanism for massive MIMO CSI FeedbackCode1
Unitary Approximate Message Passing for Matrix Factorization—0
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications—0
Low-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing—0
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems—0
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive SensingCode1
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing—0
Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints—0
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet—0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors—0
Compressive Sensing with Wigner D-functions on Subsets of the Sphere—0
Memory-efficient model-based deep learning with convergence and robustness guarantees—0
Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking—0
Single pixel imaging at high pixel resolutions—0
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions—0
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive ImagingCode0
Evaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation—0
Nonconvex L_ 1/2 -Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction—0
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds—0
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural NetworksCode1
Signal Recovery with Non-Expansive Generative Network Priors—0
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed SensingCode1
FSOINet: Feature-Space Optimization-Inspired Network for Image Compressive SensingCode1
Blind Orthogonal Least Squares based Compressive Spectrum Sensing—0
A General Compressive Sensing Construct using Density Evolution—0
Adaptive and Cascaded Compressive Sensing—0
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
The Role of Interactivity in Structured Estimation—0
One-Bit Compressive Sensing: Can We Go Deep and Blind?—0
Coarse-to-Fine Sparse Transformer for Hyperspectral Image ReconstructionCode0
_1DecNet+: A new architecture framework by _1 decomposition and iteration unfolding for sparse feature segmentation—0
HDNet: High-resolution Dual-domain Learning for Spectral Compressive ImagingCode0
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation—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