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 151–160 of 597 papers

TitleStatusHype
Monotonically Convergent Regularization by Denoising—0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Ensemble learning priors unfolding for scalable Snapshot Compressive SensingCode1
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive SensingCode1
Unsupervised Sparse Unmixing of Atmospheric Trace Gases from Hyperspectral Satellite Data—0
Low dosage 3D volume fluorescence microscopy imaging using compressive sensing—0
Generative adversarial network for super-resolution imaging through a fiber—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
An Off-grid Compressive Sensing Strategy for the Subarray Synthesis of Non-uniform Linear Arrays—0
CSformer: Bridging Convolution and Transformer for Compressive SensingCode1
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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