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 621–630 of 3874 papers

TitleStatusHype
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
End-to-end Alternating Optimization for Blind Super ResolutionCode1
Deep Space-Time Video Upsampling NetworksCode1
Enhanced Quadratic Video InterpolationCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNNCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified