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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 480274, 12 pages
Research Article

An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic

College of Automation, Chongqing University, Chongqing 400030, China

Received 20 March 2013; Revised 22 May 2013; Accepted 11 June 2013

Academic Editor: Marco Perez-Cisneros

Copyright © 2013 Shuhan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters.