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 2461–2470 of 3874 papers

TitleStatusHype
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-ResolutionCode1
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions—0
Robust Reference-based Super-Resolution via C2-MatchingCode1
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity EstimationCode1
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution—0
A survey of machine learning-based physics event generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified