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Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm

机译：使用混合图像融合算法的监视应用中的多传感器图像融合

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摘要

Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. In this paper, a hybrid image fusion algorithm for multi focus and multi modality images is presented by exploiting the advantages of both the transform as well as spatial domain techniques. In the initial image fusion framework, the source images are decomposed only once using cascaded wavelet transform and the transformed coefficients are combined according to the fusion rules. Inverse cascaded wavelet transform is applied for obtaining the initial fused image. Further, Roberts operator is used for extracting the edge information and decision rule is introduced for choosing the edges from the focused part. The extracted edge information from the focused part replaces the existing edge information in the initial fused image for enhancing the reliability of the fused image. Experiments on various types of images such as multifocus as well as multimodality images are conducted to examine the performance of the proposed algorithm. Experimental results have shown that the proposed algorithm outperforms the well known techniques in terms of both visual perception and quantitative evaluation. Furthermore, the proposed algorithm achieves a good balance between enhancing fusion quality meanwhile reducing the computational cost.
机译：图像融合是将多个源图像集成到一个图像中的过程，该图像可提供更可靠的信息并减少冗余。通过利用变换和空间域技术的优点，提出了一种用于多焦点和多形态图像的混合图像融合算法。在初始图像融合框架中，使用级联小波变换仅对源图像进行一次分解，并根据融合规则对变换后的系数进行合并。逆级联小波变换用于获得初始融合图像。此外，使用罗伯茨算子提取边缘信息，并引入决策规则从聚焦部分中选择边缘。从聚焦部分提取的边缘信息替换了初始融合图像中的现有边缘信息，以增强融合图像的可靠性。进行了各种类型的图像（如多焦点和多模式图像）的实验，以检验所提出算法的性能。实验结果表明，该算法在视觉感知和定量评估方面均优于众所周知的技术。此外，提出的算法在提高融合质量的同时降低了计算成本之间达到了良好的平衡。

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