红外图像具有对比度低和信噪比低等特点,实用中必须进行增强处理。将小波分析与模糊逻辑相结合,提出了一种基于二代小波变换的红外图像非线性增强算法。该算法首先利用二代小波变换对图像进行分解,提取图像的多尺度细节特征,然后,根据目标和背景噪声信号的差异,通过模糊非线性增强算子分别对各个分解层的高频子带进行非线性增强来改变目标特征的强度,抑制背景信号,最后利用小波反变换重构图像,以实现图像的对比度增强和背景抑制。与几种常用的图像增强算法实验结果相比,此算法能有效地抑制图像中的背景噪声,增强目标内容信息,取得了较好的增强效果。

Because infrared image has the characteristics of low contrast and low signal-to-noise ratio, it is necessary to be enhanced. A second-generation wavelet transform based infrared image nonlinear enhancement algorithm is presented. The method is adopted to decompose the input infrared image, which extracts multi-scale detail features of the image. Then, according to the difference between target and background noise signal, a fuzzy nonlinear enhancement operator is used to enhance the details of target feature intensity under different scale. Finally, the inverse transform of wavelet is applied to reconstruct image. The algorithm can avoid over-enhanced noise and raise image contrast. Compared with other several image enhancement algorithms, several groups of experimental results demonstrate that the presented algorithm enhance content information the infrared images target effectively.