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 1531–1540 of 3874 papers

TitleStatusHype
Generating Unobserved Alternatives—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—0
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified