Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination
Salt and pepper (SAP) noise elimination is a crucial step for further image processing and pattern recognition applications. The main aim of this article is to propose a novel SAP noise elimination method which employs a regression-based neuro-fuzzy network for highly corrupted gray scale and color...
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| Veröffentlicht in: | IEEE transactions on fuzzy systems 2020-06, Vol.28 (6), p.1084-1095 |
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| container_title | IEEE transactions on fuzzy systems |
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| creator | Caliskan, Abdullah Cil, Zeynel Abidin Badem, Hasan Karaboga, Dervis |
| description | Salt and pepper (SAP) noise elimination is a crucial step for further image processing and pattern recognition applications. The main aim of this article is to propose a novel SAP noise elimination method which employs a regression-based neuro-fuzzy network for highly corrupted gray scale and color images. In the proposed method, multiple neuro-fuzzy filters trained with artificial bee colony algorithm is combined with a decision tree algorithm. The performance of the proposed filter is compared with a number of well known methods with respect to popular metrics including, structural similarity index, peak signal-to-noise ratio, and correlation on well known test images. The results reveal that the proposed filter has superior performance in terms of all comparison metrics. |
| doi_str_mv | 10.1109/TFUZZ.2020.2973123 |
| format | Article |
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The main aim of this article is to propose a novel SAP noise elimination method which employs a regression-based neuro-fuzzy network for highly corrupted gray scale and color images. In the proposed method, multiple neuro-fuzzy filters trained with artificial bee colony algorithm is combined with a decision tree algorithm. The performance of the proposed filter is compared with a number of well known methods with respect to popular metrics including, structural similarity index, peak signal-to-noise ratio, and correlation on well known test images. 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| subjects | Algorithms Artificial bee colony (ABC) Artificial bee colony algorithm Artificial neural networks Color imagery decision tree (DT) Decision trees Fuzzy logic Fuzzy neural networks Fuzzy systems Image edge detection Image processing impulse noise Microsoft Windows neuro-fuzzy (NF) network Noise Noise measurement Noise reduction Object recognition Pattern recognition Search algorithms Signal to noise ratio Swarm intelligence |
| title | Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination |
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