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 951–960 of 3874 papers

TitleStatusHype
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-ResolutionCode1
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and LatencyCode1
mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectraCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
Learning to Super-Resolve Blurry Images with EventsCode1
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby ReferencesCode1
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified