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 2671–2680 of 3874 papers

TitleStatusHype
StyleGAN-induced data-driven regularization for inverse problems—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction—0
SUNLayer: Stable denoising with generative networks—0
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified