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 291–300 of 597 papers

TitleStatusHype
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
Robust Symbol Detection in Overloaded NOMA Systems—0
Compressive sensing based privacy for fall detection—0
On Recoverability of Randomly Compressed Tensors with Low CP Rank—0
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
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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