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 3561–3570 of 3874 papers

TitleStatusHype
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
Emu Edit: Precise Image Editing via Recognition and Generation Tasks—0
Adversarial Audio Super-Resolution with Unsupervised Feature Losses—0
FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
Face hallucination using cascaded super-resolution and identity priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified