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 1041–1050 of 3874 papers

TitleStatusHype
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Exploiting Raw Images for Real-Scene Super-ResolutionCode1
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
Face Hallucination via Split-Attention in Split-Attention NetworkCode1
DL4DS -- Deep Learning for empirical DownScalingCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion TeacherCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified