Image Denoising Using A New Hybrid Neuro- Fuzzy Filtering Technique

Digital images are often contaminated by impulse noise during image acquisition and/or transmission over communication channel. A new Hybrid Neuro-Fuzzy (HNF) filter for restoring digital images corrupted by impulse noise is proposed in this paper. The proposed filter is a hybrid filter obtained by aptly combining a Nonlinear Filter (NF), Canny Edge Detector (CED) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The internal parameters of the neuro-fuzzy network are adaptively optimized by training of well known images. The most distinctive feature of the proposed filter offers excellent line, edge, and fine detail preservation performance and also effectively removes impulse noise from the image. Extensive simulation results show that the proposed hybrid filter can be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image. The performance of the proposed hybrid filter is compared with median based filters and hybrid filter [16] and shown to be more effective in terms of eliminating impulse noise and preserving edges and fine details of digital images.