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 531–540 of 3874 papers

TitleStatusHype
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Deep Image PriorCode1
Advancing High-Resolution Video-Language Representation with Large-Scale Video TranscriptionsCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified