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 301–310 of 597 papers

TitleStatusHype
Minimum-fuel Spacecraft Rendezvous based on Sparsity Promoting Optimization—0
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction—0
Model-based Decentralized Bayesian Algorithm for Distributed Compressed Sensing—0
Modular Sparse Conical Multi-beam Phased Array Design for Air Traffic Control Radar—0
Moment Transform-Based Compressive Sensing in Image Processing—0
Monotonically Convergent Regularization by Denoising—0
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing—0
More is Less: Inducing Sparsity via Overparameterization—0
MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction—0
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing—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