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 1321–1330 of 3874 papers

TitleStatusHype
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Store and Fetch Immediately: Everything Is All You Need for Space-Time Video Super-resolutionCode0
Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image—0
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology ImageCode1
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine LearningCode1
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling—0
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Directional diffusion models for graph representation learning—0
Minimalist and High-Quality Panoramic Imaging with PSF-aware TransformersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified