Parametric wiener filter with parameters estimation on image power spectrum sparsity
Naw Jacklin Nyunt, Yosuke Sugiura, Tetsuya Shimamura · 2017
This paper presents a method to estimate noise variance from the power spectrum of the observed noisy image and proposes an improved Wiener Filter called parametric Wiener filter. The best performance can be obtained with the best parameters of the parametric Wiener filter. However, in practice, it is impossible to know the best parameters because the best parameters are determined depending on the characteristics of the image. Thus, to obtain the estimated best parameters for the parametric Wiener filter, we propose a method of calculating a power spectrum sparsity of the observed noisy image. For the parametric Wiener filter, an image with a large power spectrum sparsity has larger parameters compared with the image with a small power spectrum sparsity. A parametric Wiener filter with the estimated parameters is used in practice. The experimental results shows that the proposed method provides better performance than that of the conventional method.