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 1921–1930 of 3874 papers

TitleStatusHype
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Constrained Diffusion Implicit Models—0
Face Recognition in Low Quality Images: A Survey—0
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
Face Hallucination using Linear Models of Coupled Sparse Support—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified