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DCTNet: deep shrinkage denoising via DCT filterbanks
Hasan Huseyin Karaoglu
*
,
Ender Mete Eksioglu
*
Corresponding author for this work
Department of Electronics and Communication Engineering
Istanbul Technical University
Research output
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Contribution to journal
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Article
›
peer-review
6
Citations (Scopus)
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Dive into the research topics of 'DCTNet: deep shrinkage denoising via DCT filterbanks'. Together they form a unique fingerprint.
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Keyphrases
Discrete Cosine Transform
100%
Shrinkage Denoising
100%
Filter Bank
100%
DCTNet
100%
Denoiser
30%
Deep Learning
20%
Deep Convolutional Neural Network (deep CNN)
20%
Transform Domain
20%
Shrinkage Algorithm
20%
Shrinkage Function
20%
Neural Network
10%
Discrete Wavelet Transform
10%
Deep Neural Network
10%
Deep Learning Methods
10%
Convolutional Neural Network
10%
Feature Map
10%
Two-dimensional Discrete Cosine Transform
10%
Transformative Learning
10%
Denoising Algorithm
10%
Inverse Discrete Cosine Transform
10%
Signal Transformation
10%
Patch-wise
10%
Deep Denoising
10%
Discrete Cosine Transform Domain
10%
Transform Function
10%
Transform Basis
10%
Block Treatment
10%
Sparsifying Transforms
10%
Deep Learning Layer
10%
Effective Production
10%
Engineering
Filterbank
100%
Deep Learning Method
100%
Block Transform
75%
Convolutional Neural Network
75%
Experimental Result
25%
Main Reason
25%
Satisfy Property
25%
Deep Neural Network
25%
Subband Transform
25%
Chemical Engineering
Deep Learning Method
100%
Neural Network
75%
Deep Neural Network
25%
Mathematics
Discrete Cosine Transform
100%
Deep Learning Method
30%
Convolutional Neural Network
23%
Discrete Wavelet Transform
7%
Deep Neural Network
7%
Shrinkage Approach
7%