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 1131–1140 of 3874 papers

TitleStatusHype
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learningCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR ApplicationsCode0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
Deep learning Framework for Mobile MicroscopyCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-MixerCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified