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 961–970 of 3874 papers

TitleStatusHype
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges—0
Conditional Neural Video Coding with Spatial-Temporal Super-Resolution—0
Combined Generative and Predictive Modeling for Speech Super-resolution—0
Lumiere: A Space-Time Diffusion Model for Video GenerationCode3
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New MethodCode0
LKFormer: Large Kernel Transformer for Infrared Image Super-ResolutionCode1
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified