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 276–300 of 597 papers

TitleStatusHype
UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing—0
Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service—0
Understanding Adversarial Attacks on Autoencoders—0
Underwater Sound Speed Profile Construction: A Review—0
Unitary Approximate Message Passing for Matrix Factorization—0
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography—0
Unsupervised Sparse Unmixing of Atmospheric Trace Gases from Hyperspectral Satellite Data—0
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access—0
Video Compressive Sensing for Dynamic MRI—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
Weighed l1 on the simplex: Compressive sensing meets locality—0
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications—0
Dynamic Proximal Unrolling Network for Compressive Imaging—0
Efficient Fourier single-pixel imaging with Gaussian random sampling—0
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling—0
Efficient Recovery of Jointly Sparse Vectors—0
Efficient Sampling for Learning Sparse Additive Models in High Dimensions—0
Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication—0
Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis—0
Energy-aware adaptive bi-Lipschitz embeddings—0
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values—0
Error Resilient Deep Compressive Sensing—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