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Proceedings Paper

Parameter optimization for image denoising based on block matching and 3D collaborative filtering
Author(s): Ramu Pedada; Emin Kugu; Jiang Li; Zhanfeng Yue; Yuzhong Shen
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Paper Abstract

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation.

Paper Details

Date Published: 27 March 2009
PDF: 12 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725925 (27 March 2009); doi: 10.1117/12.812202
Show Author Affiliations
Ramu Pedada, Old Dominion Univ. (United States)
Emin Kugu, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)
Zhanfeng Yue, Precision Imaging Systems (United States)
Yuzhong Shen, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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