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 3481–3490 of 3874 papers

TitleStatusHype
Super-Resolution Perception for Industrial Sensor Data—0
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency—0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
Task-Aware Image Downscaling—0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Spatio-temporal Transformer Network for Video Restoration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified