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 521–530 of 597 papers

TitleStatusHype
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices—0
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction—0
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction—0
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior—0
Tree-Structure Bayesian Compressive Sensing for Video—0
Two-stage Geometric Information Guided Image Reconstruction—0
Image Classification with A Deep Network Model based on Compressive Sensing—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
A fast patch-dictionary method for whole image recovery—0
Multichannel Compressive Sensing MRI Using Noiselet Encoding—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