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 2221–2230 of 3874 papers

TitleStatusHype
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions—0
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data—0
Fourier Neural Operator based surrogates for CO_2 storage in realistic geologies—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation—0
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
Frame and Feature-Context Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified