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 526–550 of 597 papers

TitleStatusHype
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
Structural Group Sparse Representation for Image Compressive Sensing Recovery—0
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images—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
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing—0
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—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