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 3121–3130 of 3874 papers

TitleStatusHype
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution—0
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models—0
Perception-Oriented Stereo Image Super-Resolution—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Perceptual Fairness in Image Restoration—0
Perceptually-inspired super-resolution of compressed videos—0
Perceptually Optimized Super Resolution—0
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified