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 3201–3250 of 3874 papers

TitleStatusHype
Analysis Operator Learning and Its Application to Image Reconstruction—0
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features—0
Video Interpolation with Diffusion Models—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network—0
Video Restoration with a Deep Plug-and-Play Prior—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
Spatio-temporal Transformer Network for Video Restoration—0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs—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
An Advanced Features Extraction Module for Remote Sensing 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
Rethinking Video Super-Resolution: Towards Diffusion-Based Methods without Motion Alignment—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
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—0
Amortised MAP Inference for Image Super-resolution—0
A Modular Conditional Diffusion Framework for Image Reconstruction—0
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement—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
A mathematical theory of super-resolution and two-point resolution—0
A mathematical theory of resolution limits for super-resolution of positive sources—0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—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
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified