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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2076–2100 of 3874 papers

TitleStatusHype
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
Learning Parametric Sparse Models for Image Super-Resolution—0
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
Deep Hierarchical Super Resolution for Scientific Data—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
Deep generative model super-resolves spatially correlated multiregional climate data—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep filter bank regression for super-resolution of anisotropic MR brain images—0
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples—0
Learning Super-Resolution Jointly from External and Internal Examples—0
Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data—0
Learning Texture Transformer Network for Light Field Super-Resolution—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Deep Blind Hyperspectral Image Fusion—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified