Two-scale image fusion of visible and infrared images using saliency detection
Two-scale image fusion of visible and infrared images using saliency detection
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DOI：
10.1016/j.infrared.2016.01.009
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发表时间：
2016-05-01
影响因子：
3.3
通讯作者：
Dhuli, Ravindra
中科院分区：
文献类型：
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作者：
Bavirisetti, Durga Prasad;Dhuli, Ravindra
军事、导航和隐藏武器检测需要不同的成像模式，如可见光和红外线，以监视目标场景。这些模式提供了补充信息。为了更好的态势感知，必须将这些图像的互补信息整合到单个图像中。图像融合是将互补的源信息整合到合成图像中的过程。提出了一种基于显著性检测和两尺度分解的图像融合方法。这种方法是有益的，因为本文介绍的视觉显著性提取过程可以突出源图像的显著性信息非常好。提出了一种新的基于视觉显著性的权值图构造方法。该过程能够将源图像的视觉显著信息整合到融合图像中。与大多数多尺度图像融合技术相比，该技术仅使用两个尺度的图像分解。所以它是快速和高效的。我们的方法进行了测试，在几个图像对，并进行了定性评估，通过目视检查和定量使用客观的融合指标。将所提出的方法的结果与最先进的多尺度融合技术进行了比较。结果表明，所提出的方法的性能是可比的或上级现有的方法。(C)2016爱思唯尔B.V.保留所有权利。
Military, navigation and concealed weapon detection need different imaging modalities such as visible and infrared to monitor a targeted scene. These modalities provide complementary information. For better situation awareness, complementary information of these images has to be integrated into a single image. Image fusion is the process of integrating complementary source information into a composite image. In this paper, we propose a new image fusion method based on saliency detection and two scale image decomposition. This method is beneficial because the visual saliency extraction process introduced in this paper can highlight the saliency information of source images very well. A-new weight map construction process based on visual saliency is proposed. This process is able to integrate the visually significant information of source images into the fused image. In contrast to most of the multi-scale image fusion techniques, proposed technique uses only two-scale image decomposition. So it is fast and efficient. Our method is tested on several image pairs and is evaluated qualitatively by visual inspection and quantitatively using objective fusion metrics. Outcomes of the proposed method are compared with the state-of-art multi-scale fusion techniques. Results reveal that the proposed method performance is comparable or superior to the existing methods. (C) 2016 Elsevier B.V. All rights reserved.