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 1801–1810 of 3874 papers

TitleStatusHype
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Deep Networks for Image and Video Super-Resolution—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
Deep multi-frame face super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified