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 31–40 of 597 papers

TitleStatusHype
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI FeedbackCode1
CSformer: Bridging Convolution and Transformer for Compressive SensingCode1
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural NetworkCode1
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—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