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Exploring the potential of channel interactions for image restoration
Yuning Cui,
Alois Knoll
nformatics 6 - Chair of IRobotics, Artificial Intelligence and Real-time Systems
Technical University of Munich
Research output
:
Contribution to journal
›
Article
›
peer-review
24
Scopus citations
Overview
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Dive into the research topics of 'Exploring the potential of channel interactions for image restoration'. Together they form a unique fingerprint.
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Keyphrases
Image Reconstruction
100%
Channel Interaction
100%
Art Performance
66%
Channel Attention
66%
Benchmark Dataset
33%
Deep Learning Architectures
33%
Convolutional Neural Network
33%
Image Pair
33%
Spatial Domain
33%
Transformer
33%
Spatial Dimension
33%
Image Denoising
33%
Long-range Dependence
33%
Representation Learning
33%
Image Motion
33%
Information Integration
33%
Dynamic Weight
33%
Dual Domain
33%
Clear Image
33%
Restored Image
33%
Desliming
33%
Defocus Deblurring
33%
Image Dehazing
33%
Dehazed Image
33%
Channel Attention Mechanism
33%
Adjacent Channel
33%
Computer Science
Interaction Channel
100%
Image Restoration
100%
Art Performance
66%
Attention (Machine Learning)
33%
Representation Learning
33%
Deep Architecture
33%
Convolutional Neural Network
33%
Image Dehazing
33%
Range Dependency
33%
Spatial Dimension
33%
Frequency Domain
33%
Spatial Domain
33%
Adjacent Channel
33%
Engineering
Image Restoration
100%
Frequency Domain
33%
Spatial Domain
33%
Image Pair
33%
Spatial Dimension
33%
Adjacent Channel
33%
Image Motion
33%
Image Dehazing
33%
Convolutional Neural Network
33%
Earth and Planetary Sciences
State of the Art
100%