一种基于二维GARCH模型的图像去噪方法

A method of image denoising based on two-dimensional GARCH model

提出了一种基于小波系数统计模型的图像去噪方法。该方法利用二维广义自回归条件异方差(2D-GARCH)模型对小波系数进行建模,这种小波系数模型能够更好地利用小波系数"尖峰厚尾"的分布特性和系数间的相关性等重要特性。利用基于果蝇优化算法的极大似然估计(ML Estimation based on FOA)代替传统的线性规划方法求解模型参数,提高了建模的准确性。在此基础上再采用最小均方误差估计(MMSE Estimation)对未受噪声污染的原始图像的小波系数进行估计。实验结果表明,与当前主流的去噪方法相比,该算法能更有效地去除图像中的噪声,获得更高的峰值信噪比(PSNR)和较好的视觉效果。

An image denoising method based on the statistical model for wavelet coefficients is proposed. It uses a two-dimensional Generalized Autoregressive Conditional Heteroscedasticity（ 2D-GARCH） model for modeling the wavelet coefficients. A novel wavelet coefficients model is also used to make better use of the important characteristics of wavelet coefficients such as ＂ sharp peak and heavy tailed＂ marginal distribution and the dependencies between the coefficients. It utilizes maximum likelihood estimation based on fruit fly optimization algorithm（ ML Estimation based on FOA） to estimate the model parameters instead of using traditional linear programming in order to improve the accuracy of the modeling. The minimum mean square error estimation（ MMSE Estimation） is applied to estimating the parameters of the wavelet coefficients of the original image that is not affected by noise. Experimental results showed that compared to the present widely-used denoising methods the proposed method is more effective in image denoising,and it may achieve higher peak signal-to-noise ratio（ PSNR） and good visuality.