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 571–580 of 3874 papers

TitleStatusHype
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Single-snapshot machine learning for super-resolution of turbulence—0
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-ResolutionCode1
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Enhancing digital core image resolution using optimal upscaling algorithm: with application to paired SEM images—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
Use of triplet loss for facial restoration in low-resolution images—0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-ResolutionCode1
Solving Video Inverse Problems Using Image Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified