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 261–270 of 597 papers

TitleStatusHype
Compressive Sensing and Neural Networks from a Statistical Learning Perspective—0
Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics—0
Bayesian Compressive Sensing Using Normal Product Priors—0
Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach—0
Active User Detection of Uplink Grant-Free SCMA in Frequency Selective Channel—0
Generative adversarial network for super-resolution imaging through a fiber—0
Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels—0
Generalized Optimization of High Capacity Compressive Imaging Systems—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—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