Adaptive edge discriminated median filter to remove impulse noise

In this article, a new approach for removal of impulse noise has been proposed. Here main emphasize is given to distinguish between an edge pixel and a noisy pixel. The proposed method is, basically, a three step method, where in the first step; the suspected pixels are detected based on their differences from neighboring pixels. In the next step, suspected pixels are categorized by edge pixels or noisy pixels. Then the noisy pixels are only replaced by median of uncorrupted pixels of the considering window. To demonstrate the effectiveness of the proposed method, results are compared with other state-of-the-art methods qualitatively and quantitatively. Results of the proposed method are found to be encouraging.

[1]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[2]  H. Wu,et al.  Adaptive impulse detection using center-weighted median filters , 2001, IEEE Signal Processing Letters.

[3]  David Ebenezer,et al.  A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.

[4]  Kai-Kuang Ma,et al.  Tri-state median filter for image denoising , 1999, IEEE Trans. Image Process..

[5]  Yiqiu Dong,et al.  A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise , 2007, IEEE Signal Processing Letters.

[6]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[7]  Sung-Jea Ko,et al.  Center weighted median filters and their applications to image enhancement , 1991 .

[8]  D. R. K. Brownrigg,et al.  The weighted median filter , 1984, CACM.

[9]  Zhe Zhou,et al.  Cognition and Removal of Impulse Noise With Uncertainty , 2012, IEEE Transactions on Image Processing.

[10]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[11]  Kai-Kuang Ma,et al.  A switching median filter with boundary discriminative noise detection for extremely corrupted images , 2006, IEEE Trans. Image Process..

[12]  Bing Li,et al.  Qualitative rules mining and reasoning based on cloud model , 2010, The 2nd International Conference on Software Engineering and Data Mining.