基于PCNN区域分割的图像邻域去噪算法  

Image De-noising Algorithm with Neighborhood Based on PCNN Segmentation

作　　者：毛瑞全[1] 宫霄霖[1] 刘开华[1] 

机构地区：[1]天津大学电子信息工程学院,天津300072

出　　处：《光电工程》2010年第2期122-127,共6页Opto-Electronic Engineering

摘　　要：针对小波图像去噪方法中使用的NeighShrink方法,本文提出了一种有效的保护图像边缘的图像去噪算法。主要改进了NeighShrink方法中固定的邻域范围,根据图像自身的性质,自适应分割成不同的邻域对图像进行去噪处理;并进一步结合小波层内相关性,对各个不规则邻域加上固定的窗口,选择了几何距离更为接近且在同一不规则邻域内的系数,以完善NeighShrink方法。该算法采取平稳小波对含噪图像进行分解,以保持相位不变性,并对低频子带利用脉冲耦合神经网络模型进行图像分割,按照一定的规则将性质相似的像素点相接,得到原图像分割后的信息。在处理过程中利用得到的分割信息对边缘予以保护。实验结果表明,该方法在降低了图像噪声的同时又尽可能地保留了图像的边缘信息,是一种有效的去噪方法。For NeighShrink method used in the image de-noising, a new image de-noising algorithm is proposed to keep image edges more effectively, and it mainly improve the domain of NeighShrink which is fixed. The new method can segment the image into many domains adaptively to de-noise the images. Furthermore, combined with wavelet correlation in the same layer, we get various irregular neighborhoods with a fixed window, and choose the coefficients which have closer geometric distance and are in the same irregular neighborhood to improve NeighShrink. This method decomposes noisy images with stationary wavelet transform to keep phase invariance. Then, in accordance with special rules, it segments the low frequency sub-band by using Pulse Coupled Neural Networks （PCNN） model, and then gets the approximate information. And the edge information will be protected during the de-noising process. A better restoration of images is demonstrated in the results of experiments, with detail of images kept as well as image noises decreasing

关 键 词：图像去噪 脉冲耦合神经网络 图像分割 自适应邻域 

分 类 号：TN391[电子电信—物理电子学] TN911.73