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 176–200 of 597 papers

TitleStatusHype
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation—0
Lottery Image Prior—0
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
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
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
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative PriorsCode0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet FramesCode0
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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