Structure Preserving Noise Filtering of Images using Explicit Local Segmentation

The trend in modern image noise filtering algorithms has been toward structure preservation by using only those neighbouring pixels which are similar to the current pixel in some way. In this paper we introduce a technique call FUELS (Filtering Using Explicit Local Segmentation) which explicitly segments the m \Theta m region encompassing the current pixel and filters using only those pixels from the same segment. By exploiting mask overlap an effective mask size of (2m \Gamma 1) \Theta (2m \Gamma 1) is obtained, as well as robustness in regions which do not fit the image model. The algorithm can be iterated, and our results show FUELS to outperform existing algorithms both quantitatively and qualitatively. 1. Introduction Noise filtering [1, 2] is the process of removing unwanted noise from an image and is often a preprocessing stage in image analysis. Thus we desire a filter to preserve any image structure such as edges and texture. Traditional linear filters such as the box and Gaus..