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 2876–2900 of 3874 papers

TitleStatusHype
Resampling and super-resolution of hexagonally sampled images using deep learning—0
A Unified Plug-and-Play Algorithm with Projected Landweber Operator for Split Convex Feasibility Problems—0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
A unified method for super-resolution recovery and real exponential-sum separation—0
Residual Feature Aggregation Network for Image Super-Resolution—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Augmenting Perceptual Super-Resolution via Image Quality Predictors—0
Residual Networks for Light Field Image Super-Resolution—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Resolution enhancement in scanning electron microscopy using deep learning—0
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence—0
Resolution Invariant Autoencoder—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified