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 501–550 of 597 papers

TitleStatusHype
Structured Sparsity: Discrete and Convex approaches—0
Ambient Occlusion via Compressive Visibility Estimation—0
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity—0
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms—0
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor CompletionCode0
Blind Compressive Sensing Framework for Collaborative Filtering—0
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared—0
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing—0
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals—0
Compressed sensing MRI using masked DCT and DFT measurements—0
LiSens --- A Scalable Architecture for Video Compressive Sensing—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting—0
Compressive Hyperspectral Imaging with Side Information—0
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images—0
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework—0
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries—0
Reconstruction-free action inference from compressive imagers—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Efficient Sampling for Learning Sparse Additive Models in High Dimensions—0
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices—0
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction—0
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction—0
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior—0
Tree-Structure Bayesian Compressive Sensing for Video—0
Two-stage Geometric Information Guided Image Reconstruction—0
Image Classification with A Deep Network Model based on Compressive Sensing—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
A fast patch-dictionary method for whole image recovery—0
Multichannel Compressive Sensing MRI Using Noiselet Encoding—0
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction—0
Group-based Sparse Representation for Image RestorationCode0
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization—0
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images—0
Structural Group Sparse Representation for Image Compressive Sensing Recovery—0
One-bit compressive sensing with norm estimation—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Compressive Pattern Matching on Multispectral Data—0
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit—0
Low-Cost Compressive Sensing for Color Video and Depth—0
Information-Theoretic Bounds for Adaptive Sparse Recovery—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—0
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing—0
Noise Analysis for Lensless Compressive Imaging—0
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)—0
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling—0
Signal to Noise Ratio in Lensless Compressive Imaging—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