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 2651–2660 of 3874 papers

TitleStatusHype
StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and SynthesisCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—0
TPU-GAN: Learning temporal coherence from dynamic point cloud sequencesCode0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning From Unpaired Data: A Variational Bayes Approach—0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified