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 351–400 of 597 papers

TitleStatusHype
Group-Sparse Model Selection: Hardness and Relaxations—0
Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—0
High-Dimensional Confidence Regions in Sparse MRI—0
High SNR Consistent Compressive Sensing—0
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
How to find real-world applications for compressive sensing—0
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration—0
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior—0
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet—0
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing—0
Identifying Unused RF Channels Using Least Matching Pursuit—0
_1DecNet+: A new architecture framework by _1 decomposition and iteration unfolding for sparse feature segmentation—0
Image Classification with A Deep Network Model based on Compressive Sensing—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization—0
Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization—0
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix—0
Image Restoration from Patch-based Compressed Sensing Measurement—0
Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters—0
Improved Coherence Index-Based Bound in Compressive Sensing—0
Information-Theoretic Bounds for Adaptive Sparse Recovery—0
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models—0
In-sector Compressive Beam Alignment for MmWave and THz Radios—0
Instance Optimal Decoding and the Restricted Isometry Property—0
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation—0
IoT Connectivity Technologies and Applications: A Survey—0
ISAR imaging of space objects using encoded apertures—0
Iterative Sparse Identification of Nonlinear Dynamics—0
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels—0
Joint group and residual sparse coding for image compressive sensing—0
Joint Localization and Information Transfer for Reconfigurable Intelligent Surface Aided Full-Duplex Systems—0
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Joint optimization for compressive video sensing and reconstruction under hardware constraints—0
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing—0
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
Kinetic Compressive Sensing—0
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems—0
Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimization—0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT—0
Lensless Imaging by Compressive Sensing—0
Lensless Imaging with Compressive Ultrafast Sensing—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework—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