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Efficiency of texture image enhancement by DCT-based filtering
Aleksey Rubel
, Vladimir Lukin
, Mikhail Uss
, Benoit Vozel
, Oleksiy Pogrebnyak
,
Karen Egiazarian
Research output
:
Contribution to journal
›
Article
›
Scientific
›
peer-review
29
Citations (Scopus)
Overview
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Dive into the research topics of 'Efficiency of texture image enhancement by DCT-based filtering'. Together they form a unique fingerprint.
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Keyphrases
Image Enhancement
100%
Image Texture
100%
Noise Removal
66%
Discrete Cosine Transform
66%
Visual Quality
33%
Filter Method
33%
Additive White Gaussian Noise
33%
Neural Network
33%
Pattern Recognition
33%
Noise Model
33%
Filter Efficiency
33%
Image Classification
33%
Statistical Parameters
33%
Detailed Structure
33%
Noise Suppression
33%
Spatially Correlated Noise
33%
Image Pattern
33%
Denoising Filter
33%
Roughness Characteristics
33%
Textural Properties
33%
Image Object
33%
Efficiency Potential
33%
Fine Texture
33%
Texture Detail
33%
Texture Roughness
33%
Recognition Classification
33%
Object Shape
33%
Noise Characteristics
33%
Computer Science
Image Enhancement
100%
Discrete Cosine Transform
100%
Visual Quality
33%
de-noising
33%
Neural Network
33%
Additive White Gaussian Noise
33%
Image Classification
33%
Correlated Noises
33%
Noise Suppression
33%
Statistical Parameter
33%
Pattern Recognition
33%
Engineering
Filtration
100%
Image Enhancement
100%
Metrics
25%
Additive White Gaussian Noise
25%
Pattern Recognition
25%
Image Classification
25%
Noise Suppression
25%
Object Shape
25%
Image Object
25%