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 1901–1910 of 3874 papers

TitleStatusHype
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Dense U-net for super-resolution with shuffle pooling layer—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
Face Super-resolution Guided by Facial Component Heatmaps—0
J-Net: Improved U-Net for Terahertz Image Super-Resolution—0
Constrained Diffusion Implicit Models—0
Face Recognition in Low Quality Images: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified