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 371–380 of 3874 papers

TitleStatusHype
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-ResolutionCode1
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Image super-resolution via dynamic networkCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image SegmentationCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Stochastic interpolants with data-dependent couplingsCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Forecasting Tropical Cyclones with Cascaded Diffusion ModelsCode1
RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency WavefrontsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified