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 2291–2300 of 3874 papers

TitleStatusHype
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
Global graph features unveiled by unsupervised geometric deep learning—0
Global-Local Face Upsampling Network—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified