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 2976–3000 of 3874 papers

TitleStatusHype
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Time accelerated image super-resolution using shallow residual feature representative network—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood EstimationCode1
Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes—0
Deformable 3D Convolution for Video Super-ResolutionCode1
Deep Space-Time Video Upsampling NetworksCode1
Lossless Image Compression through Super-ResolutionCode2
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency RegularizationCode1
Unsupervised Real-world Image Super Resolution via Domain-distance Aware TrainingCode1
Robust Single-Image Super-Resolution via CNNs and TV-TV MinimizationCode1
Feature-Driven Super-Resolution for Object Detection—0
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New StrategyCode1
Space-Time-Aware Multi-Resolution Video EnhancementCode1
When to Use Convolutional Neural Networks for Inverse Problems—0
Super Resolution for Root ImagingCode0
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified