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 351–360 of 3874 papers

TitleStatusHype
Conditional Variational Diffusion ModelsCode1
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolutionCode1
Spatial-Temporal Contrasting for Fine-Grained Urban Flow InferenceCode1
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-ResolutionCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
LFSRDiff: Light Field Image Super-Resolution via Diffusion ModelsCode1
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion ModelsCode1
Image Super-Resolution with Text Prompt DiffusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified