Multi resolution bilateral filter for MR image denoising

• Main Authors: Mustafa, Z A, Kadah, Y M
• Format: Conference Proceeding
• Language: English
• Subjects: bilateral filter; denoising algorithms; Filtering algorithms; Image denoising; image quality factor; Image resolution; Information filters; magnetic resonance imaging; method noise; multi resolution; Noise; Noise reduction; wavelet transform
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/MECBME.2011.5752095

Clinical magnetic resonance imaging (MRI) data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to increase the : Signal-to-Noise Ratio (SNR) and improve image quality. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. In this paper we propose an extension of the bilateral filter: multi resolution bilateral filter (MRBF), with wavelet transform (WT) sub-bands mixing. The proposed wavelet sub-bands mixing is based on a multi resolution approach for improving the quality of image denoising filter, which turns out to be very effective in eliminating noise in noisy images. Quantitative validation was carried out on synthetic datasets generated with the Brain Web simulator. Comparison with other methods, such as nonlinear diffusion, Fourth-Order Partial Differential Equations, Total variation, Nonlocal mean, Wavelet thresholding, and Bilateral filters, shows that the proposed multi resolution bilateral filter (MRBF) produces better denoising results. The mathematical analysis is based on the analysis of the "method noise", defined as the difference between a digital image and its denoised version. The MRBF algorithm is also proven to be asymptotically optimal under a generic statistical image model. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.