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 2601–2650 of 3874 papers

TitleStatusHype
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Sparsity-Based Super Resolution for SEM Images—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
Spatio-temporal Transformer Network for Video Restoration—0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning—0
Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data—0
Spectral Bayesian Uncertainty for Image Super-Resolution—0
Spectral Compressed Sensing via Structured Matrix Completion—0
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution—0
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution—0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction—0
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution—0
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation—0
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation—0
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions—0
Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering—0
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices—0
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution—0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network—0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations—0
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution—0
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution—0
STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution—0
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution—0
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
Regional climate risk assessment from climate models using probabilistic machine learning—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified