A Globallobal lobal -Local Noise Removal Approach to Remove High Density Impulse Noise

Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse noise; however, the image quality obtained in high noise density is not desirable. The aim of this paper is to propose an algorithm in order to improve the performance of adaptive median filter to remove high density impulse noise from digital images. The proposed method consists of two main stages of noise detection and noise removal. In the first stage, noise detection includes two global and local phases and in the second stage, noise removal is also done based on a two-phase algorithm. Global noise detection is done by a pixel classification approach in each block of the image and local noise detection is performed by automatically determining two threshold values in each block. In the noise removal stage only noise pixels detected from the first stage of the algorithm are processed by estimating noise density and applying adaptive median filter on noise-free pixels in the neighborhood. Comparing experimental results obtained on standard images with other proposed methods proves the success of the proposed algorithm.

Keywords:

Impulse Noise , Noise Detection , Noise Removal , Adaptive Median Filter