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 3691–3700 of 3874 papers

TitleStatusHype
Volumetric Super-Resolution of Multispectral Data—0
Signal reconstruction via operator guiding—0
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding—0
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
Face Super-Resolution Through Wasserstein GANsCode1
Super-Resolution of Wavelet-Encoded Images—0
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified