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 2151–2160 of 3874 papers

TitleStatusHype
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection—0
Exploring the solution space of linear inverse problems with GAN latent geometry—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
Face Hallucination using Linear Models of Coupled Sparse Support—0
Face Recognition in Low Quality Images: A Survey—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified