一种边缘优化的红外与可见光图像模块化融合方法

A Modular Fusion Method Based on Edge Optimization Infrared and Visible Image

针对红外与可见光图像融合中出现的对比度较低和图像模糊的问题,提出了一种边缘优化的模块化融合方法.首先,对红外图像进行局部对比度自适应增强,突出红外图像中的目标;然后分别对红外图像和可见光图像进行HSV色空间变换,在亮度分量中,对其进行边缘细节增强的模块化融合,进一步突出红外图像中的目标轮廓;最后,结合可见光图像的色调和饱和度分量,经过RGB色空间的转换,获取融合后的图像.结果表明,与简单加权平均算法和小波融合算法的融合结果相比较,该融合算法能够较好的保持原图像的细节和目标信息,图像的对比度和清晰度也有较大的提高.

To deal with the problem of low contrast and image fuzzy in the fusion processing for infrared and visible image,a modular fusion method based on edge optimization infrared and visi- ble image is proposed. Firstly,the infrared image is enhanced by the local contrast adaptive meth- od,and highlighted the target of the infrared image;then,the infrared image and visible image is transformed separately by the color space of HSV,in the obtained intensity components,two of them are fused by the modular fusion rule of edge details enhancement, further highlighted the infrared image of the target profile; Finally, combining the hue and saturation of visible image, new intensity component are transformed into the RGB color space,and obtained the fusion im- age. By subjective evaluation and objective evaluation standard,experiment results show that the results of this fusion algorithm are better than the simple weighted average algorithm and wave- let fusion algorithm,which can reserve the original image details and target information,and also improve greatly the contrast and resolution of the image.