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 326–350 of 597 papers

TitleStatusHype
Nonconvex L_ 1/2 -Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction—0
Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation—0
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm—0
Nonconvex penalties with analytical solutions for one-bit compressive sensing—0
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration—0
Non-Local Compressive Sensing Based SAR Tomography—0
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Object tracking in video signals using Compressive Sensing—0
Off-grid Multi-Source Passive Localization Using a Moving Array—0
One-Bit Compressive Sensing: Can We Go Deep and Blind?—0
One-bit compressive sensing with norm estimation—0
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing—0
On Generalization Bounds for Deep Compound Gaussian Neural Networks—0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach—0
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
On Recoverability of Randomly Compressed Tensors with Low CP Rank—0
Onsager-corrected deep learning for sparse linear inverse problems—0
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements—0
A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
Optimal Data Detection and Signal Estimation in Systems with Input Noise—0
Optimal Sensor Placement and Enhanced Sparsity for Classification—0
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding—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