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 711–720 of 3874 papers

TitleStatusHype
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-ResolutionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
Improving the Fairness of Deep Generative Models without RetrainingCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Increasing the accuracy and resolution of precipitation forecasts using deep generative modelsCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Instant recovery of shape from spectrum via latent space connectionsCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified