SOTAVerified

Rolling Shutter Correction

Rolling Shutter Correction

Papers

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Showing 151–160 of 216 papers

TitleStatusHype
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
DNU: Deep Non-Local Unrolling for Computational Spectral Imaging—0
Deep Shutter Unrolling NetworkCode1
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction—0
Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling—0
Rolling-Unrolling LSTMs for Action Anticipation from First-Person VideoCode1
Orthogonal Over-Parameterized TrainingCode1
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness—0
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image ProcessingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DFRSC-3FramesAverage PSNR (dB)34.48—Unverified
2QRSC-5FramesAverage PSNR (dB)33.5—Unverified
3DFRSC-2FramesAverage PSNR (dB)33.39—Unverified
4JAMNet-2FramesAverage PSNR (dB)32.93—Unverified
5AdaRSC-3FramesAverage PSNR (dB)28.23—Unverified
6SUNet-2FramesAverage PSNR (dB)27.76—Unverified
7JCDAverage PSNR (dB)25.59—Unverified
8DSUNAverage PSNR (dB)25.21—Unverified