基于非下采样Contourlet变换耦合特征选择机制的可见光与红外图像融合算法

为了克服当下较多可见光与红外图像融合方法因忽略了光谱特征而导致融合图像存在光谱扭曲、目标内容显著度较差等不足,提出了非下采样Contourlet变换(nonsubsampled contourlet transform, NSCT)耦合特征选择机制的图像融合算法。首先,通过NSCT对可见光与红外图像计算,分离出其不同图像系数。然后,利用信息熵函数,度量图像所含信息量的丰富度,以形成低频系数的融合系数,得到富含红外目标等丰富信息的融合低频系数。采用像素点的邻点信息,度量图像的清晰度特征,并引入均值函数,度量图像的光谱特征,再联合图像的清晰度特征,构造特征选择机制,从图像中选择理想的高频系数融合函数,获取兼顾细节特征和光谱特征的融合高频系数。最后,通过实验结果发现,较现有的融合算法而言,所提算法拥有更好的融合质量,更好地保持了图像的光谱特征,且目标内容显著。

Infrared and visible image fusion algorithm based on nonsubsampled contourlet transform coupled with feature selection mechanism

In order to overcome the shortcomings as spectral distortion and poor target content saliency of the fused image by ignoring spectral features in current visible and infrared image fusion methods, the infrared and visible image fusion algorithm based on nonsubsampled contourlet transform coupled with feature selection mechanism is proposed in this paper. Firstly, the visible and infrared images are calculated by NSCT to separate it into different image coefficients. Then, the information entropy function is used to measure the richness of the image information content for forming the fusion coefficient of low-frequency coefficient, which can obtain the fusion low-frequency coefficient with rich information such as infrared target. The neighborhood information of pixels was used to measure the definition feature of image, and the mean function was introduced to measure the spectral feature of image. Through the definition feature and the spectral feature of image, the feature selection mechanism was constructed to select the ideal high-frequency coefficient fusion function from the image, and obtain the fused high-frequency coefficient that takes into account both the detailed characteristics and the spectral characteristics. Finally, the experimental results show that compared with the existing fusion algorithm, the proposed algorithm has better spectral characteristics, significant target content and better fusion performance.