A Spatial Mean and Median Filter For NoiseRemoval in Digital Images

Abstract

In this project, Mean and Median image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into account the surrounding pixels to derive a more accurate version of this pixel. By taking neighbouring pixels into consideration, extreme “noisy” pixels can be replaced. However, outlier pixels may represent uncorrupted fine details, which may be lost due to the smoothing process. This project examines two common smoothing algorithms. These algorithms can be applied to one-dimensional as well as two-dimensional signals. For each of the two algorithms discussed, experimental results will be shown that indicate which algorithm is best suited for the purpose of impulse noise removal in digital color images.