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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 597 papers

TitleStatusHype
A Spatially Separable Attention Mechanism for massive MIMO CSI FeedbackCode1
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive ImagingCode1
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive SensingCode1
Measuring Robustness in Deep Learning Based Compressive SensingCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI FeedbackCode1
CSformer: Bridging Convolution and Transformer for Compressive SensingCode1
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive SensingCode1
Optimization-Inspired Cross-Attention Transformer 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