An efficient PDE-Based nonlinear filter adapted to Rician noise for restoration and enhancement of magnetic resonance images

摘要

In the present manuscript, a PDE based nonlinear filter adapted to Rician noise is proposed for removal of Rician noise from MR images. The proposed method is casted into a variational framework. The introduced filter consists of two terms wherein the first term is a data fidelity term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of Rician pdf. Since the solution of the first term is ill-posed in nature and hence a prior function is introduced which is a nonlinear anisotropic diffusion based filter. To balance the trade off between data fidelity term and prior a regularization parameter has been introduced. The performance analysis and comparative study of the proposed method with other standard methods is presented for Brain Web dataset at varying noise levels in terms of PSNR and SSIM. From the simulation results, it is observed that the proposed method is performing better noise removal in comparison to other methods.