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 3371–3380 of 3874 papers

TitleStatusHype
To learn image super-resolution, use a GAN to learn how to do image degradation firstCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
Super-Resolution Neural OperatorCode0
Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality DecouplingCode0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset ChallengeCode0
Shepard Convolutional Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified