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 1241–1250 of 3874 papers

TitleStatusHype
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
End-to-end Alternating Optimization for Real-World Blind Super ResolutionCode1
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile DeviceCode1
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
CMISR: Circular Medical Image Super-Resolution—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-ResolutionCode1
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment TechnologyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified