一种基于内容增强的可见光-红外线图像融合方法

为更好地保留可见光图像的丰富细节信息, 提出一种基于方向导波的动态图像压缩增强方法对可见光图像和红外线图像进行融合. 首先, 利用方向导波增强方法对可见光图像进行增强; 其次, 对增强后的可见光图像和对应的红外线图像利用非下采样轮廓波进行分解, 得到一个低频子带和一系列高频子带, 用稀疏表示理论对低频子带进行融合, 用参数自适应的脉冲耦合神经网络对各高频子带进行融合; 最后, 利用非下采样轮廓波的逆变换得到融合图像. 实验结果表明, 该方法在客观质量评价和视觉检测方面均优于传统方法.

Fusion Method Based on Content Enhancement for Visible-Infrared Image

In order to better preserve the rich details of the visible image, we proposed a dynamic image compression enhancement method based on directional guided wave to fuse visible image and infrared image. Firstly, the directional guided wave enhancement method was used to enhance the visible image. Secondly, a low-frequency subband and a series of high-frequency subbands were obtained by decomposing the enhanced visible image and the corresponding infrared image with the nonsubsampled contourlet. A sparse representation theory was used to fuse the low-frequency subband. The parametric adaptive impulse coupled neural network was used to fuse each high-frequency subband. Finally, the fused image was obtained by the inverse transform of nonsubsampled contourlet. The experimental results show that the proposed method is superior to the traditional methods in objective quality evaluation and visual inspection.