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 101–125 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
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified