SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2791–2800 of 3874 papers

TitleStatusHype
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy—0
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Multitask Learning for VVC Quality Enhancement and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified