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
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