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 911–920 of 3874 papers

TitleStatusHype
Efficient scene text image super-resolution with semantic guidanceCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-ExemplarsCode1
edge-SR: Super-Resolution For The MassesCode1
Sampling Generative NetworksCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified