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 861–870 of 3874 papers

TitleStatusHype
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Masked Autoencoders are PDE LearnersCode0
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections—0
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
A Study in Dataset Pruning for Image Super-Resolution—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified