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Cortical dipole imaging has been developed to visualize brain electrical activity in high spatial resolution. It is necessary to solve an inverse problem to estimate the cortical dipole distribution from the scalp potentials. In the present study, the accuracy of cortical dipole imaging was improved by focusing on filtering property of the spatial...
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The goal of this paper is to propose a novel diffusion adaptive algorithm for estimation of sparse system with non- Gaussian noise. The proposed adaptive algorithm uses zero-norm regularization for accelerating the speed of convergence in sparse conditions and it uses maximum correntropy criterion (MCC) to access lower steady-state error in the pre...
Citations
... If the absolute difference of paired pixels is larger than a predefined threshold, the corresponding pixel is declared as corrupted; otherwise, it is declared as noise-free. To obtain an estimate of the noise-free image, a median and weighted medianbased impulse detector is devised in [6]; the ACWMF is used in [7]; an improved ACWMF is proposed in [8] where if the noise density is larger than 30%, the sliding window shape of the IACWMF is changed; otherwise, it reverts to ACWMF; a nonlocal median filter is utilized in [9]; a spatial inverse filter is employed in [10]. Estimation of a noise-free pixel determines whether the pixel is corrupted, not the final output. ...
... One is the TVD method (see (6)), where the noise-free and noisy pixels are processed uniformly. The other is the NSDD (see (10)) [23]. TVD uses a 0.5 time step size. ...
Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.
