A hybrid filter based on an adaptive neuro-fuzzy inference system for efficient removal of impulse noise from corrupted digital images

Abstract

A new impulse noise detector based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filtering, a wiener filtering and the ANFIS. The noise is exactly estimated through the proposed operator. The internal parameters of the ANFIS are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The distinctive feature of the proposed operator is that it offers well line, edge, detail and texture preservation performance while, at the same time, effectively removing noise from the input image. Simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.