摘要
针对红外图像对比度差、信噪比低的特点,提出了一种基于小波变换和灰度形态学的红外图像对比度增强的算法,对红外图像进行小波分解后,利用灰度形态学对低频系数进行对比度增强,同时计算局部阈值,并利用确定的阈值对小波系数进行去噪处理,最后重构得到去噪后的增强图像。实验结果表明,本文算法有效的提高了目标的对比度,同时突出了目标的细节信息,算法在性能优于传统的中值滤波与直方图均衡法相结合、维纳滤波与灰度变换法相结合的对比度增强算法。
For infrared image with low contrast and low signal-to-noise ratio,a infrared image enhancement method based on Wavelet Transform and Grayscale Morphology is presented.The wavelet transform is adopted to decompose the input infrared image,then low frequency coefficients are enhanced by the Grayscale Morphology.At the same time the calculated part threshold value is used for image de-noising.Finally,the inverse wavelet transform is applied to synthesis image which can obtain the enhanced image.A group of experimental results demonstrate that the presented algorithm not only solves the problem of the low contrast in infrared image,but also reduces the noise and highlight the image detail.The proposed algorithm outperforms the traditional image enhancement methods of Median Filtering method with histogram equalization enhancement and wiener Filtering method with gray value transform.
出处
《激光与红外》
CAS
CSCD
北大核心
2011年第6期683-686,共4页
Laser & Infrared
关键词
小波变换
灰度形态学
对比度增强
局部阈值去噪
wavelet transform
grayscale morphology
contrast enhancement
part threshold de-noising
