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 3271–3280 of 3874 papers

TitleStatusHype
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Feature Aggregation Network for Image Super-Resolution—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Residual Networks for Light Field Image Super-Resolution—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified