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 141–150 of 597 papers

TitleStatusHype
A General Compressive Sensing Construct using Density Evolution—0
Adaptive and Cascaded Compressive Sensing—0
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
The Role of Interactivity in Structured Estimation—0
One-Bit Compressive Sensing: Can We Go Deep and Blind?—0
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction—0
_1DecNet+: A new architecture framework by _1 decomposition and iteration unfolding for sparse feature segmentation—0
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging—0
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral 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