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 281–290 of 597 papers

TitleStatusHype
Block based Adaptive Compressive Sensing with Sampling Rate Control—0
Adaptive low rank and sparse decomposition of video using compressive sensing—0
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares—0
Compressive Single-pixel Fourier Transform Imaging using Structured Illumination—0
Blind Orthogonal Least Squares based Compressive Spectrum Sensing—0
Compressive Shift Retrieval—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Blind Compressive Sensing Framework for Collaborative Filtering—0
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems—0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—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