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 2801–2810 of 3874 papers

TitleStatusHype
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Enhanced Quadratic Video InterpolationCode1
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified