Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination
Citation
Caliskan, A., Cil, Z.A., Badem, H., Karaboga, D. (2020). Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination. IEEE Transactions on Fuzzy Systems, 28 (6), art. no. 8995478, pp. 1084-1095.Abstract
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.