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 3341–3350 of 3874 papers

TitleStatusHype
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Semantic uncertainty intervals for disentangled latent spacesCode0
Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-ResolutionCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
Multi Kernel Estimation based Object SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified