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 1741–1750 of 3874 papers

TitleStatusHype
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
Text-guided Explorable Image Super-resolution—0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified