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 1011–1020 of 3874 papers

TitleStatusHype
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
CNN-generated images are surprisingly easy to spot... for nowCode1
Scale-wise Convolution for Image RestorationCode1
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
High-dimensional Dense Residual Convolutional Neural Network for Light Field ReconstructionCode1
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified