由于可见光图像在低光照环境下其可视性较差, 为了提高红外与弱可见光图像融合的效果, 提出了一种基于对比度增强和柯西模糊函数的图像融合算法.首先用改进的引导滤波自适应增强提高弱可见光图像暗区域的可视性; 其次, 利用非下采样剪切波变换将红外和增强后的弱可见光图像分解, 得到相应的低频和高频子带; 再后, 分别用直觉模糊集构建柯西隶属函数和自适应双通道脉冲发放皮层模型对低频、高频子带进行融合; 最后, 使用非下采样剪切波变换对融合得到的高低频子带进行逆变换重构得到融合图像.实验结果表明, 与其它融合算法相比, 该算法有效地增强了弱可见光图像的暗区域, 保留了更多的背景信息, 从而提高了融合图像的对比度和清晰度.

Due to the poor visibility of visible images in low-light environment, an image fusion algorithm based on contrast enhancement and cauchy fuzzy function is proposed to improve the fusion effect of infrared and low-light-level visible images. Firstly, the visibility of dark region of low-light-level visible image is improved by the adaptive enhancement of improved guided filtering. Secondly, non-subsampled shearlet transform is used to decompose infrared and enhanced low-light-level visible images to obtain corresponding low-frequency and high-frequency components. Then, the intuitive fuzzy sets were used to construct the cauchy membership function and adaptive dual - channel spiking cortical model to fuse the low-frequency and high-frequency components. Finally, the fusion image are reconstructed by using non-subsampled shearlet inverse transform. Experimental results show that compared with other fusion algorithms, the algorithm can effectively enhance the dark area of the low-light-level visible image and retain more background information, thus improving the contrast and clarity of the fusion image.