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 476–500 of 597 papers

TitleStatusHype
Onsager-corrected deep learning for sparse linear inverse problems—0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets—0
Masking Strategies for Image Manifolds—0
Sketching for Large-Scale Learning of Mixture Models—0
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources—0
Compressive phase-only filtering at extreme compression rates—0
Deep Fully-Connected Networks for Video Compressive SensingCode0
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography—0
Multi-resolution Compressive Sensing Reconstruction—0
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing—0
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Compressive hyperspectral imaging via adaptive sampling and dictionary learning—0
Fast and Accurate Head Pose Estimation via Random Projection Forests—0
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior—0
Frequency-Based Environment Matting by Compressive Sensing—0
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing—0
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
Quantifying the influence of conformational uncertainty in biomolecular solvation—0
Compressive Sensing via Low-Rank Gaussian Mixture Models—0
Distributed Compressive Sensing: A Deep Learning Approach—0
A Deep Learning Approach to Structured Signal Recovery—0
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing—0
Toward a Robust Sparse Data Representation for Wireless Sensor Networks—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