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 2381–2390 of 3874 papers

TitleStatusHype
Pyramidal Denoising Diffusion Probabilistic Models—0
High Dynamic Range and Super-Resolution from Raw Image Bursts—0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified