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 526–550 of 3874 papers

TitleStatusHype
Cross-Resolution Flow Propagation for Foveated Video Super-ResolutionCode1
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-ResolutionCode1
Infrared Image Super-Resolution: Systematic Review, and Future TrendsCode1
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
Mitigating Artifacts in Real-World Video Super-Resolution ModelsCode1
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image FusionCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal ShiftsCode1
Learning Continuous Depth Representation via Geometric Spatial AggregatorCode1
Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormersCode1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Global Learnable Attention for Single Image Super-ResolutionCode1
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantificationCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Perception-Oriented Single Image Super-Resolution using Optimal Objective EstimationCode1
GAN Prior based Null-Space Learning for Consistent Super-ResolutionCode1
Guided Depth Super-Resolution by Deep Anisotropic DiffusionCode1
N-Gram in Swin Transformers for Efficient Lightweight Image Super-ResolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion ModuleCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified