<title>Adaptive varying-window-size image denoising using the ICI rule with median filters</title>

Abstract

We describe a novel approach to solve a problem of window size selection for median based filtering of noisy images. The approach is based on the intersection of confidence intervals (ICI) rule and results in algorithms that are simple in implementation. The ICI rule gives the adaptive varying bandwidths and enables the algorithm to be spatially adaptive in the sense that its quality is close to that which one could achieve if the smoothness of the estimated signal were known in advance. We propose and analyze a two-stage structure for the median based adaptive filter with different use of the ICI rule. At the first stage (segmentation), the ICI rule with the median filter is applied in order to find the adaptive window size for every pixel of the image. At the second stage (filtering), the image denoising is produced by a weighted median filter with varying window sizes obtained at the first stage. Two different approaches (with a single centered window and with combined four-quadrant windows), affecting the structure of the filters, have been considered in order to form the local neighborhood of the targeted pixel. Comparison of the developed algorithm with known techniques for noise removal shows the advantage of the new adaptive window size approach, both quantitatively and visually.