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 101–150 of 3874 papers

TitleStatusHype
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed ObservationsCode2
Generative Diffusion-based Downscaling for ClimateCode2
Latent Modulated Function for Computational Optimal Continuous Image RepresentationCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolutionCode2
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-ResolutionCode2
SRGS: Super-Resolution 3D Gaussian SplattingCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
Rethinking Diffusion Model for Multi-Contrast MRI Super-ResolutionCode2
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
GenN2N: Generative NeRF2NeRF TranslationCode2
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion DistillationCode2
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Adaptive Super Resolution For One-Shot Talking-Head GenerationCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Misalignment-Robust Frequency Distribution Loss for Image TransformationCode2
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token DictionaryCode2
Video Super-Resolution Transformer with Masked Inter&Intra-Frame AttentionCode2
Transforming Image Super-Resolution: A ConvFormer-based Efficient ApproachCode2
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion ModelsCode2
Kandinsky 3.0 Technical ReportCode2
Zooming Out on Zooming In: Advancing Super-Resolution for Remote SensingCode2
Neural Fields with Thermal Activations for Arbitrary-Scale Super-ResolutionCode2
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Enhancing Video Super-Resolution via Implicit Resampling-based AlignmentCode2
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified