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 131–140 of 597 papers

TitleStatusHype
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
Graph-based Semi-supervised Local Clustering with Few Labeled NodesCode0
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares—0
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
ISAR imaging of space objects using encoded apertures—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
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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