This paper focuses on impulsive noise filtering and outliers rejection in gray-scale images. The proposed method combines neural networks, lower-upper-middle (LUM) smoothers and adaptive switching operations to produce a high-quality enhanced image. Extensive experimentation reported in this paper indicates that the proposed method is sufficiently robust, achieves an excellent balance between noise suppression and signal-detail preservation, and outperforms some well-known filters both subjectively and objectively.
