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 271–280 of 597 papers

TitleStatusHype
Generative adversarial network for super-resolution imaging through a fiber—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Compressive Sensing of ECG Signals using Plug-and-Play Regularization—0
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring—0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems—0
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing—0
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization—0
Group Sparse Coding with a Laplacian Scale Mixture Prior—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