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 241–250 of 3874 papers

TitleStatusHype
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified