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 761–770 of 3874 papers

TitleStatusHype
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-ResolutionCode1
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral ImageryCode1
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-ResolutionCode1
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-ResolutionCode1
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Light Field Image Super-Resolution Using Deformable ConvolutionCode1
Attention Beats Linear for Fast Implicit Neural Representation GenerationCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified