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 2051–2100 of 3874 papers

TitleStatusHype
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
Learning From Unpaired Data: A Variational Bayes Approach—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Deep learning at scale for subgrid modeling in turbulent flows—0
Towards the Automation of Deep Image Prior—0
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
Deep Image Super Resolution via Natural Image Priors—0
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks—0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
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