基于小波变换与特征提取的红外弱小目标图像融合

Image Fusion of Infrared Weak-Small Target Based on Wavelet Transform and Feature Extraction

当前红外单波段数据不能全面反映图像细节以及轮廓信息,弱小目标成像后难以抵抗背景干扰,使得图像产生较低的信噪比。因此有必要利用不同波段数据的纹理差异性,通过互补融合方法提高图像的信噪比。基于此,提出一种基于小波变换与特征提取的融合方法。首先将多源图像进行多尺度二维分解,获得各图像的低频信息与高频信息,在此基础上,高频信息采取绝对值取大的融合方法,低频信息采取加权求平均的融合方法,进而重构图像。然后,利用特征提取方法得到中波与长波特征图像。最后对重构图像与红外中长波特征图像进行对比度调制再融合。融合结果与多种融合算法进行对比。实验结果表明,该算法能够增强图像中弱小目标的灰度,可以很好地识别目标,解决了图像中弱小目标抗背景干扰的问题。

The image details and contour information cannot be fully reflected for the current infrared single-band data.It is difficult for the weak-small target to resist background interference after imaging,so that the image produces a low ratio of signal-to-noise.Therefore,it is necessary to use the texture difference of different band data to improve the signal-to-noise ratio of the image by using the complementary fusion method.Based on the above-mentioned,a fusion method based on wavelet transform and feature extraction is proposed.Firstly,the source images are multi-scale and two-dimensionally decomposed to obtain low-frequency information and high-frequency information.And that,the high-frequency information adopt the method of maximizing the absolute value,the low-frequency information adopt the method of weighted averaging,and reconstruct the image.Then,the infrared feature extraction method is used to obtain the medium wave and long wave feature images.Finally,the reconstructed image is contrast-modulated and refused with the medium-long wave infrared feature image.The fusion results are compared with a variety of fusion algorithms.The experimental results show that the algorithm can enhance the gray scale of weak-small targets in the image,which can identify the target well and solve the problem of weak target against background interference in infrared images.