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 1391–1400 of 3874 papers

TitleStatusHype
Global-Local Face Upsampling Network—0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Attention-based Image Upsampling—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Data-Driven Design for Fourier Ptychographic Microscopy—0
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified