The SURE-LET approach using hybrid thresholding function for image denoising. (August 2018) - Zhang, Xiaobo
Highlights: Original SURE-LET thresholding function is analyzed in detail. A new SURE-LET thresholding function is formed. The proposed modification is very simple. The new method achieves the good performance. Abstract: The Stein's unbiased risk estimate and the linear expansion of thresholds (SURE-LET) approach proposed by Luisier et al. is very efficient for image denoising. But the SURE-LET approach adopts the pointwise thresholding function excluding the intrascale information in the wavelet transform, which limits the denoising ability of the technique. In this paper, to improve the SURE-LET approach, a hybrid thresholding function is designed by incorporating the local Wiener filter into the pointwise thresholding function. Experimental results show that the new hybrid thresholding function gets better denoising performance objectively and subjectively compared to the related SURE-LET approaches and the state-of-the-art wavelet-based methods. Graphical abstract: