Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control
This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficie...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/10/208 |
| _version_ | 1797573395764740096 |
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| author | Xiao-Xia Yin Sillas Hadjiloucas |
| author_facet | Xiao-Xia Yin Sillas Hadjiloucas |
| author_sort | Xiao-Xia Yin |
| collection | DOAJ |
| description | This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation. |
| first_indexed | 2024-03-10T21:09:03Z |
| format | Article |
| id | doaj.art-9fec55a35e3441ea80c6125d8971b81b |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T21:09:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-9fec55a35e3441ea80c6125d8971b81b2023-11-19T16:56:18ZengMDPI AGJournal of Imaging2313-433X2023-09-0191020810.3390/jimaging9100208Digital Filtering Techniques Using Fuzzy-Rules Based Logic ControlXiao-Xia Yin0Sillas Hadjiloucas1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, ChinaDivision of Bioengineering, School of Biological Sciences, University of Reading, Reading RG6 6AY, UKThis paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.https://www.mdpi.com/2313-433X/9/10/208fuzzy filterimage processingcolor image sequencesmultichannel filteringneuro-fuzzy network |
| spellingShingle | Xiao-Xia Yin Sillas Hadjiloucas Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control Journal of Imaging fuzzy filter image processing color image sequences multichannel filtering neuro-fuzzy network |
| title | Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control |
| title_full | Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control |
| title_fullStr | Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control |
| title_full_unstemmed | Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control |
| title_short | Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control |
| title_sort | digital filtering techniques using fuzzy rules based logic control |
| topic | fuzzy filter image processing color image sequences multichannel filtering neuro-fuzzy network |
| url | https://www.mdpi.com/2313-433X/9/10/208 |
| work_keys_str_mv | AT xiaoxiayin digitalfilteringtechniquesusingfuzzyrulesbasedlogiccontrol AT sillashadjiloucas digitalfilteringtechniquesusingfuzzyrulesbasedlogiccontrol |