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
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Asynchronous Multi Agent Active Search—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
An Ensemble Approach for Compressive Sensing with Quantum—0
The Power of Triply Complementary Priors for Image Compressive Sensing—0
Provable Convergence of Plug-and-Play Priors with MMSE denoisers—0
Site-specific online compressive beam codebook learning in mmWave vehicular communication—0
Identifying Unused RF Channels Using Least Matching Pursuit—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave Massive MIMO Systems from 1-Bit Measurements—0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
Recovering compressed images for automatic crack segmentation using generative models—0
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing—0
IoT Connectivity Technologies and Applications: A Survey—0
Composing Normalizing Flows for Inverse Problems—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
Restricted Structural Random Matrix for Compressive Sensing—0
Multilinear Compressive Learning with Prior KnowledgeCode0
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative PriorsCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder—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