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 2731–2740 of 3874 papers

TitleStatusHype
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Light Field Super-Resolution With Zero-Shot Learning—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras—0
Space-Time Distillation for Video Super-Resolution—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified