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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 597 papers

TitleStatusHype
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)—0
Recovering compressed images for automatic crack segmentation using generative models—0
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm—0
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Regional Total Electron Content Map Generation based on Compressive Sensing—0
Regularizing linear inverse problems with convolutional neural networks—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Remote Multilinear Compressive Learning with Adaptive Compression—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Compressive Sensing with Wigner D-functions on Subsets of the Sphere—0
Restricted Structural Random Matrix for Compressive Sensing—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
Review of Algorithms for Compressive Sensing of Images—0
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity—0
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors—0
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—0
Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences—0
Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints—0
Robust Dequantized Compressive Sensing—0
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover—0
Robust Mean Estimation in High Dimensions via _0 Minimization—0
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models—0
Sampling and Reconstruction of Sparse Signals in Shift-Invariant Spaces: Generalized Shannon's Theorem Meets Compressive Sensing—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