Neural Network with Median Filter for Image Noise Reduction

Abstract According to recent advances in Digital devices, the problem of image noise reduction becomes more significant than ago. Median filter (MF), as an efficient solution for this problem, has been widely applied in practice. In this paper, to improve the quality of filtered image, using a Neural Network (NN) is proposed. A NN, which is trained in a real time manner, can be estimated the noise density of moving window/mask in MF and changes its size adaptively. By using the NN as a supervisor for MF, better performance can be achieved. Simulation results are obtained to show the ability of the proposed combination in image noise reduction. © 2012 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of Information Engineering Research Institute Keywrds: Image processing, noise reduction, Median filter, neural network. 1. Introduction One of the major research fields in image processing is noise reduction [1]. The acquisition or transmission of digital images through sensors or communication channels is often interfered by impulse noise. Impulse noise randomly and sparsely corrupts pixels to two intensity levels, high or low, when compared with its neighboring pixels. Typically, salt-and-pepper noise, which is a special case of impulse noise, is considered in this situation [2-5]. In many applications such as military, medical and media, noise reduction plays a significant role. So, many filters/techniques have been proposed by different authors for image noise reduction. In addition, noise reduction in image processing not only is used to improve image