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 3061–3070 of 3874 papers

TitleStatusHype
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network—0
A parametric non-negative coupled canonical polyadic decomposition algorithm for hyperspectral super-resolution—0
Semi-Supervised Super-Resolution—0
4KAgent: Agentic Any Image to 4K Super-Resolution—0
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect—0
A Papier-Mâché Approach to Learning 3D Surface Generation—0
Separation-Free Spectral Super-Resolution via Convex Optimization—0
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach—0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution—0
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified