Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function
[摘要] Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications.
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[效力级别] [学科分类] 肠胃与肝脏病学
[关键词] image denoising;medical image processing;probability;discrete wavelet transforms;partial differential equations;biodiffusion;Gaussian noise;biomedical MRI;image denoising;bidimensional empirical mode decomposition;Student's probability distribution function;BEMD sifting process;tBEMD domain;fourth order partial differential equation;linear complex diffusion process;nonlinear complex diffusion process;discrete wavelet transform;biomedical images;standard digital image;additive Gaussian noise;peak signal-to-noise ratio;PDE;LCDP;NLCDP;DWT;classical BEMD domain;clinical applications [时效性]
