小波变换结合MIVP识别图像脉冲噪声的研究

Research of image impulse noise recognition based on wavelet-transform combined with MIVP method

提高脉冲噪声的识别率是提高去除脉冲噪声效果的关键。利用小波变换检测信号奇异点的原理,小波变换可用于识别信号中的脉冲噪声。实验表明,在小波变换识别数字图像的脉冲噪声时,由于将受到脉冲噪声污染的像素点判别为未受脉冲噪声污染的像素点的误判率较高,影响了小波变换识别脉冲噪声的整体精度。为了有效解决这一问题,提出了一种基于统计理论的数字图像脉冲噪声统计量识别法称之为MIVP法,可以弥补小波变换误判噪声点为非噪声点的不足。以小波变换结合MIVP法为基础构成图像脉冲噪声滤波器,在不增加时间复杂度的条件下,有效提高了脉冲噪声的滤波效果。

The rightness of recognizing impulse noise is the key to the effectiveness of denoising.Wavelet transform can detect impulse noise in signals based on the principle that it can detect signal＇s singular point.Experimental study shows that the total noise identification accuracy is affected by the error rate that a pixel polluted by impulse noise may be identified as a normal pixel when wavelet transform is used to identify impulse noise.In order to effectively address this issue,a statistical theory based method called MIVP method is proposed for identifying impulse noise in digital image which can make up for the weakness that a noised pixel may be identified as a normal pixel by wavelet transform.An image impulse noise filter is designed based on the wavelet transform combined with MIVP method.Such a filter can effectively improve the effectiveness of filtering impulse noise without increasing its time complexity.