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 511–520 of 597 papers

TitleStatusHype
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
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Recursions Are All You Need: Towards Efficient Deep Unfolding NetworksCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
An Efficient Method for Robust Projection Matrix DesignCode0
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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