【摘要】 本文提出一种采用非局部主成分分析的极大似然估计去噪方法.首先采用非局部主成分分析算法来计算像素邻域间的灰度值和纹理结构相似性,然后通过极大似然估计方法估计最优复原图像.本方法使用非局部主成分分析克服现有局部性去噪方法模糊边界等缺陷,引入极大似然估计方法来改进现有非局部均值的简单加权均值去噪处理,从而提高对图像细节信息的复原能力.最后分别使用本文方法、非局部均值和局部极大似然估计三种去噪方法,在不同噪音大小和不同几何纹理复杂度的图像中进行定性和定量的去噪实验.结果表明,本文方法可在保持图像细节和纹理信息的情况下有效去噪,较之现有方法效果更好.更多还原

【Abstract】 A maximum likelihood estimation image denoising method is proposed using the non-local principle component analysis.Pixels with high similarity in both the gray level and the texture information are selected,and used to implement the maximum likelihood estimation.This kind of optimal restored method can overcome the drawback of the local image denoising method such as blurring edge,and improve the accuracy for restoring detail information in image using maximum likelihood estimation.Experiments using the proposed method,principal neighborhood dictionary non-local mean method and local maximum likelihood estimation method are implemented in images with different noise standard and different geometric complexity,and the performance of aboving three denoising methods are compared quantitatively and qualitatively.The results demonstrate that the proposed method can remove the noise effectively and preserve detail imformation of images compared with the currently used methods.更多还原