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Joint image filtering with deep convolutional networks
Yijun Li
, Jia Bin Huang
,
Narendra Ahuja
, Ming Hsuan Yang
Electrical and Computer Engineering
Siebel School of Computing and Data Science
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
Research output
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peer-review
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Keyphrases
Guidance Image
100%
Deep Convolutional Network
100%
Joint Image Filtering
100%
Target Image
50%
Joint Filter
50%
Image Data
25%
Learning-based
25%
Spatial Resolution
25%
State-of-the-art Techniques
25%
Depth Image
25%
Filter Method
25%
Structural Details
25%
RGB Image
25%
Convolutional Neural Network
25%
Image Filter
25%
Salient Structure
25%
Filter Construction
25%
RGB-NIR
25%
NIR Imaging
25%
Implicative Filter
25%
Engineering
Joints (Structural Components)
100%
Image Filtering
100%
Target Image
50%
Spatial Resolution
25%
Objective Function
25%
Structural Detail
25%
State-of-the-Art Method
25%
Image Filter
25%
Convolutional Neural Network
25%
Computer Science
Image Filtering
100%
Convolutional Neural Network
100%
Objective Function
50%
Spatial Resolution
50%
Structural Detail
50%