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 201–210 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
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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