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 1081–1090 of 3874 papers

TitleStatusHype
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion ModelsCode1
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Image Super-Resolution with Text Prompt DiffusionCode1
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy—0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech SynthesisCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified