基于改进CoF的红外与可见光图像融合

Infrared and visible image fusion based on improved CoF

为了使红外与可见光图像融合能够保留图像边缘信息和背景信息,提出改进共现滤波算法。首先分析共现滤波效果取决于滤波尺度标准差、图像内容;接着基于像素强度偏斜度和基于像素能量对滤波尺度优化,自适应确定像素对距离阈值提高了滤波的速度;最后NSST算法对图像的低、高频子带使用不同融合方法,低频子带图像的融合使用Delaunay插值计算、最大对称环绕显著性方法,高频子带图像的融合使用修正拉普拉斯和方法。实验仿真表明,本文算法融合结果边缘细节部分更丰富,没有伪影出现,主观视觉和客观指标上均具有明显的优势。

To preserve image edge and background information during the fusion of infrared and visible images,an improved co-occurrence filtering algorithm is proposed in this paper.It is first analyzed that the co-occurrence filtering effect depends on the filter scale standard deviation and the image content.Secondly,the filtering scale is optimized based on skewness of pixel intensity and pixel energy,and filtering speed is improved by adjusting the threshold for pixel-to-pixel distance.Finally,the NSST algorithm uses different fusion methods for the low and high frequency subbands of the image,and the low-frequency subbands of the image are fused using Delaunay interpolation calculation,the maximum symmetric surround saliency method,and the sum modified Laplacian method is used for fusion of high-frequency sub-band images.The experimental simulation demonstrates that the fusion results of this paper′s algorithm are richer in the edge detail part,no artifacts appear,and have obvious advantages in both subjective vision and objective indexes.