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 2231–2240 of 3874 papers

TitleStatusHype
Continual Learning Approaches for Anomaly DetectionCode0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors—0
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering—0
Neural Volume Super-Resolution—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified