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 3331–3340 of 3874 papers

TitleStatusHype
Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion StrategiesCode0
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT SimulationCode0
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-ResolutionCode0
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model ApproachCode0
Multi-Resolution Data Fusion for Super Resolution ImagingCode0
Variational Mixture of HyperGenerators for Learning Distributions Over FunctionsCode0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Multi-Objective Reinforced Evolution in Mobile Neural Architecture SearchCode0
A Cone-Beam X-Ray CT Data Collection designed for Machine LearningCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified