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 321–330 of 597 papers

TitleStatusHype
A Hybrid Architecture for On-Device Compressive Machine Learning—0
Phase Retrieval using Untrained Neural Network Priors—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models—0
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning—0
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values—0
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models—0
Multi-Channel Deep Networks for Block-Based Image Compressive SensingCode0
A Bayesian Lasso based Sparse Learning Model—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