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 161–170 of 597 papers

TitleStatusHype
Compressive Scanning Transmission Electron Microscopy—0
More is Less: Inducing Sparsity via Overparameterization—0
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive ImagingCode1
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative ModelsCode0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image ReconstructionCode3
Moment Transform-Based Compressive Sensing in Image Processing—0
Optimizing Binary Symptom Checkers via Approximate Message Passing—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—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