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 1361–1370 of 3874 papers

TitleStatusHype
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
A Generative Model for Generic Light Field Reconstruction—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
A Boosting Method to Face Image Super-resolution—0
Augmenting Perceptual Super-Resolution via Image Quality Predictors—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified