Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform
Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform
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DOI：
10.1109/jsen.2015.2478655
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发表时间：
2016-01-01
影响因子：
4.3
通讯作者：
Dhuli, Ravindra
中科院分区：
文献类型：
--
作者：
Bavirisetti, Durga Prasad;Dhuli, Ravindra
图像融合是从一组源图像中生成信息更丰富的图像的过程。图像融合的主要应用领域是导航和军事。在这里，红外和可见光传感器被用来捕捉目标场景的互补图像。利用一些融合算法将这些源图像的互补信息融合到一幅图像中。任何一种融合方法的目的都是以最小的信息损失将最大的信息从源图像传递到融合图像。它必须最小化融合图像中的伪影。针对红外和可见光传感器图像，提出了一种新的边缘保持融合方法。各向异性扩散用于将源图像分解为近似层和细节层。利用卡尔胡宁-洛夫变换和加权线性叠加法分别计算最终细节层和近似层。融合图像是由最终细节层和近似层的线性组合生成的。利用Petrovic度量对该算法的性能进行了评估。将该算法的结果与传统图像融合算法和最新的图像融合算法进行了比较。结果表明，该方法的性能优于已有的方法。
Image fusion is a process of generating a more informative image from a set of source images. Major applications of image fusion are in navigation and military. Here, infrared and visible sensors are used to capture complementary images of the targeted scene. The complementary information of these source images has to be integrated into a single image using some fusion algorithms. The aim of any fusion method is to transfer maximum information from the source images to the fused image with a minimum information loss. It has to minimize the artifacts in the fused image. In this paper, we propose a new edge preserving image fusion method for infrared and visible sensor images. Anisotropic diffusion is used to decompose the source images into approximation and detail layers. Final detail and approximation layers are calculated with the help of Karhunen-Loeve transform and weighted linear superposition, respectively. A fused image is generated from the linear combination of final detail and approximation layers. Performance of the proposed algorithm is assessed with the help of petrovic metrics. The results of the proposed algorithm are compared with the traditional and recent image fusion algorithms. Results reveal that the proposed method outperforms the existing methods.