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 761–770 of 3874 papers

TitleStatusHype
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel SynthesisCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target GenerationCode1
One-to-many Approach for Improving Super-ResolutionCode1
Practical Single-Image Super-Resolution Using Look-Up TableCode1
Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution EvaluationCode1
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving ObjectsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified