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 2131–2140 of 3874 papers

TitleStatusHype
Light field super resolution through controlled micro-shifts of light field sensor—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
Light Field Super-Resolution Via Graph-Based Regularization—0
Light Field Super-Resolution With Zero-Shot Learning—0
Light Stage Super-Resolution: Continuous High-Frequency Relighting—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
Debiased Subjective Assessment of Real-World Image Enhancement—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
Show:102550
← PrevPage 214 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified