An Optimized Neuro-Fuzzy Network based Image Denoising Techniques
M. Soranamageswari, M. Sindhana Devi · 2015
Neuro-Fuzzy (NF) system is used in various field of research. Neuro-Fuzzy systems combining neural networks and fuzzy set theories can be employed as powerful tools for the removal of impulse noise from various images. Neuro-Fuzzy system is based on a fuzzy system which is trained by learning algorithm derived from neural network theory. The learning procedure operates on local information, and causes local modifications in underlying fuzzy system. Image noise is random (not present in the object image) variation or brightness or color information in images, and is generally an aspect of electronic noise. It can be produced by sensor and circuitry of scanner or a digital camera. Images are often corrupted by noise during the acquisition or transmission process. The goal of denoising is to remove the noise while retaining as much as possible the important signal features of an image. Neuro-Fuzzy network based impulse noise filtering for gray scale images is presented. The proposed method is constructed by hybrid technique of Mamdani and Sugeno based fuzzy interference system approach followed by Optimized intelligent water drop(IWD) technique, Tuning parameter approach, Optimized fuzzy intelligence noise filter approach. As demonstrated by experimental results, Peak signal-to-noise ratio(PSNR) and Root Mean Square Error(RMSE) possess better performance. The proposed filter has some advantages over its competitors. The hybrid rule is applied to reduce the error of the optimization. In hybrid based fuzzy interference system approach, the system develops a fuzzy logic based scheme to filter a noisy signal. This can be applied in various sources to reduce noise. A one input and output Mamdani fuzzy interference system is designed for the filter where the input is a noisy signal and output is a filtered output. Fuzzy rules have been used to obtain the filtered output.