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 401–410 of 597 papers

TitleStatusHype
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration—0
Instance Optimal Decoding and the Restricted Isometry Property—0
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable GuaranteesCode0
Comparison of threshold-based algorithms for sparse signal recovery—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Light Field Reconstructions using Deep Learning—0
Perceptual Compressive SensingCode0
Full Image Recover for Block-Based Compressive SensingCode0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—0
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning—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