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Learning deep edge prior for image denoising
Yingying Fang
, Tieyong Zeng
*
*
Corresponding author for this work
Department of Mathematics
Research output
:
Contribution to journal
›
Journal article
›
peer-review
35
Citations (Scopus)
Overview
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Dive into the research topics of 'Learning deep edge prior for image denoising'. Together they form a unique fingerprint.
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Keyphrases
Image Denoising
100%
Convolutional Neural Network
100%
Edge-preserving Regularization
100%
Edge Prior
100%
Depth Edge
100%
Variational Model
50%
Total Variation Regularization
50%
Noisy Image
50%
Splitting Method
50%
Superior Performance
50%
Denoising Method
50%
Image Reconstruction
50%
Adaptivity
50%
Feature Extraction
50%
Solution Uniqueness
50%
Neural Network Method
50%
Sharp Edge
50%
De-noising Scheme
50%
Bregman
50%
Staircase
50%
Denoising Model
50%
Edge Feature
50%
Fine Texture
50%
High-quality Reconstruction
50%
Computer Science
Deep Learning Method
100%
Regularization
100%
image denoising
100%
Convolutional Neural Network
50%
de-noising
50%
Total Variation
25%
Image Restoration
25%
Feature Extraction
25%
Superior Performance
25%
Denoising Scheme
25%
Recovered Image
25%
Engineering
Deep Learning Method
100%
Regularization
100%
Convolutional Neural Network
50%
Total Variation
25%
Noisy Image
25%
Image Restoration
25%
Feature Extraction
25%
Denoising Scheme
25%
Recovered Image
25%