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 2511–2520 of 3874 papers

TitleStatusHype
Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition—0
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites—0
Self-Tuned Deep Super Resolution—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified