Q. Zhang and S.-i. Kamata (Japan)
Image denoising, Orthonormal Wavelet transform, Stein’sunbiased risk estimate (SURE), Distance constraint.
Image denoising is a lively research ﬁeld now. For solving
this problem, non-linear ﬁlters based methods are the classical approach. These methods are based on local analysis
of pixels with a moving window in spatial domain, but also
have some shortcoming. Recently, because of the properties of Wavelet transform, this research has been focused
on the wavelet domain. Compared to the classical nonlinear ﬁlters, the global multi-scale analysis characteristic
of Wavelet is better for image denoising. So this paper proposed a new approach to use orthonormal Wavelet transform and distance constraint to solve this. Here, by minimizing the Stein’s unbiased risk estimate (SURE) method
to calculate the low frequency sub-band images for estimating. And convert the high frequency sub-band images
to feature space, then use distance constraint to denoise by
trained samples set. The experimental results show that the
proposed method is efﬁciency and keep the detail ideally.