A Noise Reduction Approach Using Dynamic Fuzzy Cognitive Maps for Vehicle Traffic Camera Images

Abstract

Noise is a generic term for data loss or corruption due to hardware or software causes on the signal. Since the images are two-dimensional signals, there are noises in this type of signal due different reasons. In addition, fuzzy cognitive maps (FCM) have a structure based on a graph theory that can produce many probing solutions today. Fuzzy cognitive maps can provide their iterations as static (fixed neighborhood values) or dynamic (variable neighborhood values) depending on the solution, which belong to interested problem. In this study, a method is presented using fuzzy cognitive maps for noise reduction in images and mean filter, which is a widely used method for noise reduction. The proposed method provide to minimize the loss of data in the noise reduction process with the average filter. In this work, FCM takes noisy and average filtered noisy image masks and accepts each pixel value in these masks as nodes. Then we update the neighborhood weights between these nodes in each iteration. The developed method has been tested primarily with different images and the performance obtained only by the method in which the average filter is applied is quite high. Then, the proposed method was tested on images of traffic monitoring systems taken from vehicle cameras. The results obtained are very successful.