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 281–290 of 3874 papers

TitleStatusHype
Pairwise Distance Distillation for Unsupervised Real-World Image Super-ResolutionCode1
Real HSI-MSI-PAN image dataset for the hyperspectral/multi-spectral/panchromatic image fusion and super-resolution fieldsCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Preserving Full Degradation Details for Blind Image Super-ResolutionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-ResolutionCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified