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 471–480 of 597 papers

TitleStatusHype
Lensless Imaging with Compressive Ultrafast Sensing—0
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach—0
An Efficient Method for Robust Projection Matrix DesignCode0
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier—0
Adaptive foveated single-pixel imaging with dynamic super-sampling—0
Onsager-corrected deep learning for sparse linear inverse problems—0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets—0
Masking Strategies for Image Manifolds—0
Sketching for Large-Scale Learning of Mixture Models—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