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 401–425 of 597 papers

TitleStatusHype
Generalized Optimization of High Capacity Compressive Imaging Systems—0
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
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms—0
Compressive sensing adaptation for polynomial chaos expansions—0
Tomographic Reconstruction using Global Statistical Prior—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis—0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
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
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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