基于滚动引导滤波的红外与可见光图像融合算法

针对红外与可见光图像融合时,易产生细节丢失、噪声抑制不佳等问题,本文提出了一种改进的滚动引导滤波融合算法.该算法充分利用了滚动引导滤波边缘和局部亮度保持特性,在通过均值滤波将输入图像分解为基础层与细节层的基础上,结合滚动引导滤波与高斯滤波获取输入图像的显著图,利用不同尺度参数的引导滤波对显著图优化得到权重图,将权重图作为权重分别指导基础层与细节层的融合,最后联合融合后的子图重构得到融合图像.针对3类测试数据进行的融合实验表明,与非下采样轮廓波变换、基于引导滤波、基于显著性检测的两个尺度的图像融合等经典方法相比,本文方法得到的融合图像不但从主观视觉效果上细节信息更丰富、目标对比度加强,并且在非线性相关信息熵、相位一致性等6项客观评价指标上均具有较好的效果.

Infrared and Visible Image Fusion Algorithm Based on the Rolling Guidance Filter

For the fusion of infrared and visible images,it is easy to produce problems such as missing detail information and suppressing less noise.In this paper,an improved fusion algorithm is proposed by applying the characteristics of a rolling guidance filter,which preserves edge and local brightness.First,the input images are decomposed into base and detail layers by mean filtering.Second,the saliency maps of the input images are obtained by combining the rolling guidance and Gaussian filters.The weight maps are then optimized by guided filters of different scales.The optimized maps are used to instruct the fusion of the base and detail layers.Finally,the fused image is reconstructed by combining the merged sub-images.The method of this paper is superior on the six indicators,such as nonlinear correlation information entropy and phase consistency,compared to the classical methods such as non-subsampled contourlet transform(NSCT),image fusion with guided filtering(GFF),and two-scale image fusion based on visual saliency(TSIFVS).