图像分割与平稳小波变换法融合红外与可见光图像

为改善红外与可见光图像融合后的视觉效果,提出一种基于图像分割和平稳小波变换的图像融合方法。首先,结合最大类间方差方法与边缘检测方法,将红外图像分割为背景部分和目标部分;然后利用平稳小波变换对红外图像的背景部分与可见光图像分别进行多尺度分解,低频部分采用区域空间频率取大融合准则,高频部分采用绝对值取大融合准则,对多尺度分解后的各层进行融合,再利用平稳小波逆变换得到融合结果;最后,对该融合结果与红外图像的目标部分采用加权求和的融合准则进行融合,得到最终的红外与可见光融合图像。实验结果表明,通过提出的方法进行红外与可见光图像融合,不仅很好地突出红外图像的目标信息,还较好地体现可见光图像的场景细节信息,视觉效果明显改善;其标准差、信息熵、互信息均优于拉普拉斯金字塔变换和小波变换等传统的融合算法。实验结果验证了方法的有效性。

Fusion method for infrared and visible light images based on image segmentation and stationary wavelet transform

A novel image fusion method based on image segmentation and stationary wavelet transform (SWT) is proposed to improve the visual effect of fused infrared and visible light images. Infrared image is firstly separated into object and background region utilizing Otsu combined with edge detection. Then a multiresolution decomposition using SWT is made to the background region of the infrared image and the visible light image. Neighborhood spatial frequency and absolute value are adopted as fusion rules in low-frequency and high-frequency coefficients. The background fused image is reconstructed by inverse SWT. The final infrared and visible light fused image is obtained by fusing the background fused image and the object region of infrared image base on weighted fusion rule. The experimental results show that the object information of the infrared image is obviously highlighted and the scene information of the visible light image is well represented. The visual effect of fused image is improved efficiently by utilizing the proposed method. The proposed method works better than the traditional Laplacian Pyramid and wavelet transform fusion algorithms in terms of standard deviation, comentropy and mutual information. Experimental results verify its effectiveness.