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 421–430 of 597 papers

TitleStatusHype
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging—0
Adaptive Measurement Network for CS Image ReconstructionCode0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
A Compressive Sensing Approach to Community Detection with Applications—0
Bayesian Compressive Sensing Using Normal Product Priors—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices—0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT—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