期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 28, 期 6, 页码 1084-1095出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2973123
关键词
Noise measurement; Microsoft Windows; Noise reduction; Fuzzy neural networks; Artificial bee colony algorithm; Decision trees; Image edge detection; Artificial bee colony (ABC); decision tree (DT); impulse noise; neuro-fuzzy (NF) network
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.
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