An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

Abstract

This paper presents an algorithm for removing highdensity impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.

An adaptive dynamically weighted median filter for impulse noise removal

“…Ng and Ma [28] first detects noise candidate the same as Duan and Zhang [26] did; it then applies the same technique to noise candidates but using 3 × 3 window this time to have final selection of noisy pixels. Lee [29] uses absolute difference of median of 5 × 5 window and average of 3 × 3 window with center pixel and compares them with a threshold. It is a time efficient algorithm for low-density noise.…”