Image Denoising Using Adaptive Neuro-Fuzzy system
Nguyễn Minh Thạnh, Mu-Song Chen · International MultiConference of Engineers and Computer Scientists · 2006
Abstract — In this paper, we propose a generalized fuzzy inference system (GFIS) in noise image processing. The GFIS is a multi-layer neuro-fuzzy structure which combines both Mamdani model and TS fuzzy model to form a hybrid fuzzy system. The GFIS can not only preserve the interpretability property of the Mamdani model but also keep the robust local stability criteria of the TS model. Simulation results indicate that the proposed model shows a high-quality restoration of filtered images for the noise model than those using median filters or wiener filters, in terms of peak signal-to-noise ratio (PSNR). Index Terms —Generalized Fuzzy Inference System, Mamdani model, TS model, PSNR I. I NTRODUCTION The image corrupted by different kinds of noises is a frequently encountered problem in image acquisition and transmission [1]. The noise comes from noisy sensors or channel transmission errors. Several kinds of noises are discussed here. The impulse noise (or salt and pepper noise) is caused by sharp, sudden disturbances in the image signal; its appearance is randomly scattered white or black (or both) pixels over the image. Gaussian noise is an idealized form of white noise, which is caused by random fluctuations in the signal. Speckle noise (or more simply just speckle) can be modeled by random values multiplied by pixel values, hence it is also called multiplicative noise. If the image signal is subject to a periodic, rather than a random disturbance, we might obtain an image corrupted by periodic noise. Usually, periodic noise requires the use of frequency domain filtering. This is because whereas the other forms of noise can be modeled as local degradations, periodic noise is a global effect. However, impulse noise, gaussian noise and speckle noise can all be cleaned by using spatial filtering techniques, such as Order Statistic Filter (OSF). Order statistic filters have been applied to image processing problems [2]. Given