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 1–10 of 597 papers

TitleStatusHype
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image ReconstructionCode3
CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive SensingCode1
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural NetworkCode1
A Spatially Separable Attention Mechanism for massive MIMO CSI FeedbackCode1
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed SensingCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
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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