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 2421–2430 of 3874 papers

TitleStatusHype
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel SynthesisCode1
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh—0
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Light Field Super-Resolution With Zero-Shot Learning—0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Image Super-Resolution With Non-Local Sparse AttentionCode1
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
Space-Time Distillation for Video Super-Resolution—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified