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 11–20 of 597 papers

TitleStatusHype
FSOINet: Feature-Space Optimization-Inspired Network for Image Compressive SensingCode1
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
Ensemble learning priors unfolding for scalable Snapshot Compressive SensingCode1
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive SensingCode1
CSformer: Bridging Convolution and Transformer for Compressive SensingCode1
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive ImagingCode1
Memory-Augmented Deep Unfolding Network for Compressive SensingCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
SNIPS: Solving Noisy Inverse Problems StochasticallyCode1
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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