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 201–250 of 597 papers

TitleStatusHype
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath ChannelsCode0
Minimum-fuel Spacecraft Rendezvous based on Sparsity Promoting Optimization—0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Remote Multilinear Compressive Learning with Adaptive Compression—0
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming—0
Modular Sparse Conical Multi-beam Phased Array Design for Air Traffic Control Radar—0
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors—0
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation—0
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation—0
Optimization of retina-like illumination patterns in ghost imaging—0
Dynamic Proximal Unrolling Network for Compressive Imaging—0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative PriorsCode0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet FramesCode0
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective—0
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Weighed l1 on the simplex: Compressive sensing meets locality—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Compressive lensless endoscopy with partial speckle scanning—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems—0
Distributed Video Adaptive Block Compressive Sensing—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
Improved Coherence Index-Based Bound in Compressive Sensing—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices—0
Study on Compressed Sensing of Action Potential—0
Scalable Deep Compressive Sensing—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
On the Fourier transform of a quantitative trait: Implications for compressive sensing—0
Understanding Adversarial Attacks on Autoencoders—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Compression Boosts Differentially Private Federated Learning—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