An Improved Adaptive Wiener Filtering Algorithm
In this paper, an anisotropic image denoising algorithm is proposed by combining a nonlinear version of the local structure tensor together with Wiener filtering, where the shape and size of smoothing windows are determined by an iteratively updated nonlinear diffusion process while those in the Wie...
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| creator | Zhibo Lu Guoen Hu Xin Wang Lushan Yang |
| description | In this paper, an anisotropic image denoising algorithm is proposed by combining a nonlinear version of the local structure tensor together with Wiener filtering, where the shape and size of smoothing windows are determined by an iteratively updated nonlinear diffusion process while those in the Wiener filter are fixed. In this way, the method is data-adaptive and helps to better preserve boundaries and reduce structure delocalization. An additive operator splitting scheme is applied to solving nonlinear diffusion equation to improve computational efficiency. In simulations, the approach exhibits better performance and significant peak signal-to-noise ratio improvement than Wiener filtering and some wavelet-based filtering schemes, particularly in edge regions |
| doi_str_mv | 10.1109/ICOSP.2006.344517 |
| format | Conference Proceeding |
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| ispartof | 2006 8th international Conference on Signal Processing, 2006, Vol.1 |
| issn | 2164-5221 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Anisotropic magnetoresistance Diffusion processes Filtering algorithms Image denoising Iterative algorithms Nonlinear equations Shape Smoothing methods Tensile stress Wiener filter |
| title | An Improved Adaptive Wiener Filtering Algorithm |
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