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 91–100 of 597 papers

TitleStatusHype
Compressed domain vibration detection and classification for distributed acoustic sensing—0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing—0
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography—0
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks—0
Signal processing with optical quadratic random sketches—0
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
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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