摘要
根据小波变换用于图像消噪的原理,结合微光图像噪声的闪烁颗粒性特点,对小波变换用于微光图像消噪时的小波基及小波分解层次的选取进行了分析,得出采用Haar小波进行一层分解即可满足微光图像消噪要求的结论。为了选取小波消噪的系数阈值,通过对三幅微光图像小波系数的直方图分析,设计了阈值选取算法,并针对微光图像,得出了消噪的经验阈值。经过仿真实验及算法复杂度的时间分析,在实时性和微光图像消噪效果之间取得了平衡。
According to the principle of the wavelet transform for removing noise in images and the glimmer-and-granule characteristics of low light level （LLL） images, the methods for choosing wavelet and decomposing levels when the wavelet transform is used to remove the noise in LLL images are analyzed. A conclusion that the noise removing requirement of LLL images can be met by using Haar wavelet and decomposing one level is reached. To choose the coefficient threshold for removing noise by wavelet transform, three histograms of wavelet coefficients are analyzed and the algorithm for threshold choosing is designed. On the basis of the designed algorithm and the characteristics of LLL images, an experiential threshold for removing noise is obtained. Through the simulation experiment and the time analysis of algorithm complexity, the trade-off between the real-time ability and the effectiveness of removing noise in LLL images is attained.
出处
《红外》
CAS
2006年第12期23-28,共6页
Infrared
关键词
小波变换消噪
微光图像噪声
小波系数阈值
系数直方图
时间复杂度
wavelet transform denoising
LLL image noise
wavelet coefficient threshold
coefficient histogram
time complicated degree
