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 971–980 of 3874 papers

TitleStatusHype
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical ImageCode1
Super-Resolution Information Enhancement For Crowd CountingCode1
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and ReconstructionCode1
EgoVSR: Towards High-Quality Egocentric Video Super-ResolutionCode1
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution EnhancementCode1
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified