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
Towards Understanding the Invertibility of Convolutional Neural Networks—0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
Deep De-Aliasing for Fast Compressive Sensing MRI—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences—0
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization—0
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration—0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
Multilinear compressive sensing and an application to convolutional linear networks—0
Tuning Free Orthogonal Matching Pursuit—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