Abstract: An adaptive image denoising method based on Empirical Mode Decomposition（EMD）is proposed to denoise image and hold the image details as many as possible simultaneously. Four one-dimension vectors are obtained by expanding the image with noise from the vertical, horizontal, left and right diagonal direction respectively. They are processed using EMD and all Intrinsic Mode Functions（IMFs）resulting from the decomposition of each one-dimension vector are denoised where the hard threshold local denoise method is employed and the proposed adaptive threshold based on the noise standard deviation is used. The de-noised IMFs are summed up. The four de-noised images are obtained by the inverse transform. The last de-noised image is achieved by calculating the mean of the four de-noised images. The experimental results show that the image with noise can be denoised and the details of the image hold effectively.

Key words: Empirical Mode Decomposition（EMD）, adaptive threshold, denoise, image processing

摘要： 为了在去噪的同时保证图像细节尽可能不被破坏，提出了利用经验模式分解（Empirical Mode Decomposition，EMD）的自适应图像去噪方法。对噪声图像按照列、行、左对角和右对角方向一维展开，分别进行EMD处理，采用提出的基于噪声标准差的自适应阈值对各个基本模式函数（Intrinsic Mode Function，IMF）进行局部硬阈值去噪，将去噪后的IMF进行反变换分别获得按照四个方向展开对应的去噪后图像，将它们加和平均得到去噪后图像。实验结果表明，提出的方法能够有效地去除图像的噪声并保留足够的图像细节。

关键词: 利用经验模式分解（EMD）, 自适应阈值, 去噪, 图像处理