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23
Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control
Xiao-Xia Yin1, Sillas Hadjiloucas2
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
Journal of Imaging
|October 27, 2023
Summary
Fuzzy logic control effectively removes impulsive noise from digital images, preserving details and edges. This approach offers fast computation and superior noise suppression, even in complex images with mixed noise types.
Area of Science:
- Image Processing
- Artificial Intelligence
- Control Systems
Background:
- Impulsive noise significantly degrades digital image quality.
- Traditional noise removal methods often struggle with edge preservation and complex noise patterns.
Purpose of the Study:
- To explore fuzzy-logic control concepts for effective impulsive noise removal in digital images.
- To enhance edge and detail preservation during image filtering.
- To present and compare various fuzzy-rule based filtering techniques.
Main Methods:
- Fuzzy-rule based logic control for noise filtering.
- Fuzzy inference using vector directional filters for RGB images.
- Fuzzy cellular automata with Moore neighborhood architecture.
- Fuzzy deep learning ensemble classifiers (CNN, RNN, LSTM, GRU) with Fuzzy Min-Max (FMM).
- Fuzzy non-local mean filter approaches.
Main Results:
- Fuzzy logic filters demonstrate high-quality edge preservation.
- Effective spatial noise suppression, particularly in complex images.
- Robust noise removal for mixed additive and impulse noise.
- Fast computational implementation of discussed algorithms.
Conclusions:
- Fuzzy-logic control offers a powerful framework for advanced digital image noise removal.
- The presented fuzzy-rule based methods significantly outperform conventional techniques in image quality and noise suppression.
- Deep learning ensembles combined with fuzzy logic show promising results for complex noise scenarios.
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