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 3391–3400 of 3874 papers

TitleStatusHype
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
Detecting Overfitting of Deep Generative Networks via Latent RecoveryCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Multi-Objective Reinforced Evolution in Mobile Neural Architecture SearchCode0
Sex-Classification from Cell-Phones Periocular Iris Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified