Abstract: In this paper, an Adaptive Generalized Gaussian Distribution (AGGD) oriented thresholding function for image de-noising is proposed. This technique utilizes a unique threshold function derived from the generalized Gaussian function obtained from the HH sub-band in the wavelet domain. Two-dimensional discrete wavelet transform is used to generate the decomposition. Having the threshold function formed by using the distribution of the high frequency wavelet HH coefficients makes the function data dependent, hence adaptive to the input image to be de-noised. Thresholding is performed in the high frequency sub-bands of the wavelet transform in the interval [-t, t], where t is calculated in terms of the standard deviation of the coefficients in the HH sub-band. After thresholding, inverse wavelet transform is applied to generate the final de-noised image. Experimental results show the superiority of the proposed technique over other alternative state-of-the-art methods in the literature.

Noorbakhsh Amiri Golilarz, Hasan Demirel and Hui Gao, “Adaptive Generalized Gaussian Distribution Oriented Thresholding Function for Image De-Noising” International Journal of Advanced Computer Science and Applications(IJACSA), 10(2), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100202

@article{Golilarz2019,
title = {Adaptive Generalized Gaussian Distribution Oriented Thresholding Function for Image De-Noising},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100202},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100202},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {2},
author = {Noorbakhsh Amiri Golilarz and Hasan Demirel and Hui Gao}
}