摘要
为了提高基于图像块先验的自然图像去噪效果,有效的去除图像中的噪声,本文利用图像块的统计特性提出一种最大期望(Expectation Maximization,简称EM)自适应的学习过程,学习图像块的先验知识,通过映射某个通用先验到指定图像生成特定的先验。提出的方法相较于标准EM算法需要较少的训练数据,并且在没有无噪图像数据库时可以应用到预滤波的图像中。实验结果表明,该算法能够实现较好的去噪效果,且优于现有的一些图像去噪算法。
In order to improve the denoising effect of natural image based-patch prior and effectively remove the noise in the image,this paper proposes Expectation Maximization adaptation learning process by using the statistical characteristics of image blocks to learn image block priors,which generates specific prior by mapping a generic prior to the specified image. Compared with the standard EM algorithm,the proposed method needs less training data,and can be applied to the pre-filtered image in the absence of clean databases. The experimental results show that the proposed algorithm is superior to the existing image denoising algorithms.
出处
《科技广场》
2017年第2期14-17,共4页
Science Mosaic
