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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1991–2000 of 3874 papers

TitleStatusHype
Distribution free uncertainty quantification in neuroscience-inspired deep operators—0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments—0
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes—0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
DM^3Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching—0
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms—0
Show:102550
← PrevPage 200 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified