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 2431–2440 of 3874 papers

TitleStatusHype
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Practical Single-Image Super-Resolution Using Look-Up TableCode1
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution EvaluationCode1
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target GenerationCode1
One-to-many Approach for Improving Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified