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 2251–2260 of 3874 papers

TitleStatusHype
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems—0
Separation-Free Spectral Super-Resolution via Convex Optimization—0
A mathematical theory of super-resolution and two-point resolution—0
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Interactive Image Manipulation with Complex Text Instructions—0
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified