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 3771–3780 of 3874 papers

TitleStatusHype
RAISR: Rapid and Accurate Image Super Resolution—0
PSyCo: Manifold Span Reduction for Super Resolution—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Some medical applications of example-based super-resolution—0
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution—0
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified