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 441–450 of 597 papers

TitleStatusHype
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples—0
Structured Sparsity: Discrete and Convex approaches—0
Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images—0
Study on Compressed Sensing of Action Potential—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid—0
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
The Power of Triply Complementary Priors for Image Compressive Sensing—0
The Role of Interactivity in Structured Estimation—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