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 2781–2790 of 3874 papers

TitleStatusHype
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Tarsier: Evolving Noise Injection in Super-Resolution GANsCode1
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Residual Feature Distillation Network for Lightweight Image Super-ResolutionCode1
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesCode1
Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change ProjectionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified