Publication Type : Conference Proceedings
Publisher : IEEE-International Conference on Advances in Engineering, Science and Management
Source : IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, Nagapattinam, Tamil Nadu, p.506-511 (2012)
Url : https://ieeexplore.ieee.org/abstract/document/6216055/metrics#metrics
ISBN : 9781467302135
Keywords : Adaptive images, Bilateral filters, Brain MRI, De-noising, Denoising methods, Filtering method, Image denoising, Linear minimum mean square error estimation, MR images, MRI Image, Multiscales, Noise estimation, Nonlocal, Quality metrics, Regularization parameters, Standard deviation, Statistics, Total variation, Visualization
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Verified : Yes
Year : 2012
Abstract : In this paper, we present an automated, adaptive image denoising method for removal of Rician noise from MRI images. The proposed method is based on the discretized total variation (TV) minimization model and the local noise estimation technique. The regularization parameter of the TV-based denoising method is adapted based on the standard deviation of noise in MRI image. The performance of the proposed method is evaluated using the brain MRI images corrupted by Rician noise with standard deviation ranging from 2 to 30. The quality of the denoised image is validated using both subjective visualization tests and objective quality metrics. The experimental results show that the proposed method achieves a significant improvement in the preservation of edges while simultaneously removing the Rician noise from a MR image. The adaptive TV filtering method is reasonably better than existing methods such as non-local filter, bilateral filter and multiscale linear minimum mean square-error estimation (LMMSE) approach. © 2012 Pillay Engineering College.
Cite this Research Publication : V. N. Varghees, Manikandan, M. S., and J. Gini, R., “Adaptive MRI image denoising using total-variation and local noise estimation”, IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012. Nagapattinam, Tamil Nadu, pp. 506-511, 2012.