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 1551–1560 of 3874 papers

TitleStatusHype
Improving Scene Text Image Super-resolution via Dual Prior Modulation NetworkCode1
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and DeblurringCode1
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operatorCode1
Guided Depth Map Super-resolution: A SurveyCode1
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-ResolutionCode1
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified