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Abstract
Magnetic resonance images tend to be contaminated with random unwanted signals called noise, due to various reasons. Noise treatment of magnetic resonance brain images is considered as an important and challenging task for proper clinical and research investigations. In this manuscript, fuzzy logic-based hybrid Rician noise filter has been proposed. Proposed filtering technique uses estimated noise variance along with local and global statistics for the construction of a robust fuzzy membership function. Constructed fuzzy membership function assigns appropriate weights to the statistical estimates, based on their noise removal and detail preservation capability. Fuzzy weighted local and non-local estimators are then used for the restoration of a noisy pixel. Detailed simulations are performed, and restoration results are computed based on well-known performance measures. Numerical and visual results show that the proposed technique gives much better restored images than the existing methodologies.
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Acknowledgments
This work (2012-0005542) was supported by Mid-career Researcher Program through NRF grant funded by the MEST. Authors would also like to thank the Higher Education Commission (HEC) of Pakistan, for financial support.
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Department of Computer Science, National University of Computer and Emerging Sciences (FAST-NU), Islamabad, Pakistan
Muhammad Sharif & Muhammad Arfan Jaffar
Signal and Image Processing Laboratory, Department of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea
Ayyaz Hussain & Tae-Sun Choi
International Islamic University, Islamabad, Pakistan
Ayyaz Hussain
CCIS, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Muhammad Arfan Jaffar
- Muhammad Sharif
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Correspondence toAyyaz Hussain.
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Sharif, M., Hussain, A., Jaffar, M.A.et al. Fuzzy-based hybrid filter for Rician noise removal.SIViP10, 215–224 (2016). https://doi.org/10.1007/s11760-014-0729-1
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