【摘要】 针对低可见光图像和红外图像的特点,提出一种基于DT-CWT的自适应图像融合算法.该算法具有好的平移不变性和方向选择性,更适合于人类视觉.先对源图像作双树复小波变换,充分考虑各尺度分解层的系数特征,对低通子带引入免疫克隆选择,根据统计评价准则定义亲和度函数,自适应获得最优融合权值;对高通子带则根据人类视觉特性定义局部方向对比度,并作为融合准则,突出和增强了各源图像的对比度与细节信息.实验结果表明:与基于小波的融合结果相比较,本文的融合算法自适应性和鲁棒性更强,较好地保护和显示了源图像中的边缘和细节信息,对比度和清晰度都有所提高.更多还原

【Abstract】 Aiming at the characteristics of low visible light images and infrared images,a novel adaptive image fusion scheme based on DT-CWT was presented.The technique has good shift-invariance and directional selectivity,and is more suitable for human vision.The DT-CWT was firstly used to perform a multiresolution decomposition of source images.By taking advantage of the characters of the coefficients,the immune clonal selection algorithm was introduced in low-pass subbands and almost optimal fused weights were obtained adaptively.To high-pass subbands,the local directive contrast was defined,which was based on human visual system.And then the contrast of fused images was enhanced and the detail information of source images was protected.The experimental results show that our fused technique is effective and the fused images have a better visual quality than their wavelet counterparts.更多还原