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 1631–1640 of 3874 papers

TitleStatusHype
Image Neural Field Diffusion Models—0
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset ChallengeCode0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
SuperGaussian: Repurposing Video Models for 3D Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified