CAJ | 학술논문

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（2DG-ARCH ）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。
제출료일충기우소파계수통계모형적도상거조방법。해방법이용이유엄의자회귀조건이방차（2DG-ARCH ）모형대소파계수진행건모，저충소파계수모형능구경호지이용소파계수“첨봉후미”적분포특성화계수간적상관성등중요특성。이용기우과승우화산법적겁대사연고계（ ML Estimation based on FOA ）대체전통적선성규화방법구해모형삼수，제고료건모적준학성。재차기출상재채용최소균방오차고계（ MMSE Estimation ）대미수조성오염적원시도상적소파계수진행고계。실험결과표명，여당전주류적거조방법상비，해산법능경유효지거제도상중적조성，획득경고적봉치신조비（ PSNR）화교호적시각효과。
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 characteris -tics of wavelet coefficients such as "sharp peak and heavy tailed"marginal distribution and the dependencies be-tween the coefficients .It utilizes maximum likelihood estimation based on fruit fly optimization algorithm ( ML Esti-mation 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 .