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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 597 papers

TitleStatusHype
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming—0
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources—0
Sparse Signal Recovery Using Markov Random Fields—0
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO—0
Spatial Channel Covariance Estimation for Hybrid Architectures Based on Tensor Decompositions—0
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images—0
Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack—0
Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions—0
Memory-efficient model-based deep learning with convergence and robustness guarantees—0
Stable and robust sampling strategies for compressive imaging—0
STAR-RIS-Enabled Simultaneous Indoor and Outdoor 3D Localization: Theoretical Analysis and Algorithmic Design—0
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Structural Group Sparse Representation for Image Compressive Sensing Recovery—0
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples—0
Structured Sparsity: Discrete and Convex approaches—0
Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images—0
Study on Compressed Sensing of Action Potential—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid—0
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
The Power of Triply Complementary Priors for Image Compressive Sensing—0
The Role of Interactivity in Structured Estimation—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