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Volume 25 Issue 3
May  2010
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Dinesh J. Peter, V. K. Govindan, Abraham T. Mathew. Nonlocal-Means Image Denoising Technique Using Robust M-Estimator[J]. Journal of Computer Science and Technology, 2010, 25(3): 623-631.
Citation: Dinesh J. Peter, V. K. Govindan, Abraham T. Mathew. Nonlocal-Means Image Denoising Technique Using Robust M-Estimator[J]. Journal of Computer Science and Technology, 2010, 25(3): 623-631.
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Nonlocal-Means Image Denoising Technique Using Robust M-Estimator

  • 1Department of Computer Science, National Institute of Technology Calicut, Calicut, 673 601, India
    2Department of Electrical Engineering, National Institute of Technology Calicut, Calicut, 673 601, India

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  • Author Bio:

    Dinesh J. Peter received his M.Tech. degree from ManonmaniamSundaranar University and is pursuing hid Ph.D. degree in computerscience at National Institute of Technology Calicut. His major researchinterests include image processing and pattern recognition. He haspublished papers in various international journals and presentedinternational conference papers.

    V. K. Govindan is a professor in the Department of ComputerScience and Engineering, National Institute of Technology Calicut. Heobtained his B.Tech. and M.Tech. degrees from National Institute ofTechnology Calicut and Ph.D. degree from Indian Institute of ScienceBangalore. His research interests are image processing, medical imaging,agent technology, pattern recognition, document imaging, operatingsystems, biometrics based authentication, mobile computing,segmentation, data compression.

    Abraham T. Mathew is a professor in the Department of ElectricalEngineering, National Institute of Technology Calicut. He obtained hisPh.D. degree from Indian Institute of Technology Delhi. His researchinterests are control systems and image processing.

  • Received Date: March 31, 2009
  • Revised Date: November 05, 2009
  • Published Date: May 04, 2010
    Abstract
  • Edge preserved smoothing techniques have gained importance for the purpose of image processing applications. A good edge preserving filter is given by nonlocal-means filter rather than any other linear model based approaches. This paper explores a different approach of nonlocal-means filter by using robust M-estimator function rather than the exponential function for its weight calculation. Here the filter output at each pixel is the weighted average of pixels with surrounding neighborhoods using the chosen robust M-estimator function. The main direction of this paper is to identify the best robust M-estimator function for nonlocal-means denoising algorithm. In order to speed up the computation, a new patch classification method is followed to eliminate the uncorrelated patches from the weighted averaging process. This patch classification approach compares favorably to existing techniques in respect of quality versus computational time. Validations using standard test images and brain atlas images have been analyzed and the results were compared with the other known methods. It is seen that there is reason to believe that the proposed refined technique has some notable points.
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