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 131–140 of 3874 papers

TitleStatusHype
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution—0
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
RepNet-VSR: Reparameterizable Architecture for High-Fidelity Video Super-Resolution—0
Regularizing Differentiable Architecture Search with Smooth Activation—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study—0
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
NTIRE 2025 Challenge on Image Super-Resolution (4): Methods and ResultsCode2
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
Equilibrium Conserving Neural Operators for Super-Resolution Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified