多焦点多模态融合:多分辨率变换研究

Michael Giansiracusa, Adam Lutz, Soundararajan Ezekiel, M. Alford, Erik Blasch, A. Bubalo, M. Thomas
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引用次数: 3

摘要

自动图像融合在生物医学诊断、夜视和目标识别等众多领域具有广泛的应用。由于使用不同的多分辨率变换可以融合多种类型的图像数据,因此在图像融合领域实现自动化是困难的。不同的图像融合变换为图像融合提供了系数,创造了大量的可能性。本文旨在了解如何从多焦点和多模态图像子域开始,为不同应用选择多分辨率变换来实现自动化。该研究分析了每个子域的最大有效性,并确定了一两个最有效的图像融合变换。对各种变换技术进行了综合比较,找出了融合输入特性与最优变换之间的关系。利用基于信息理论、基于图像特征和基于结构相似度的无参考图像融合指标完成评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-focus and multi-modal fusion: a study of multi-resolution transforms
Automated image fusion has a wide range of applications across a multitude of fields such as biomedical diagnostics, night vision, and target recognition. Automation in the field of image fusion is difficult because there are many types of imagery data that can be fused using different multi-resolution transforms. The different image fusion transforms provide coefficients for image fusion, creating a large number of possibilities. This paper seeks to understand how automation could be conceived for selected the multiresolution transform for different applications, starting in the multifocus and multi-modal image sub-domains. The study analyzes the greatest effectiveness for each sub-domain, as well as identifying one or two transforms that are most effective for image fusion. The transform techniques are compared comprehensively to find a correlation between the fusion input characteristics and the optimal transform. The assessment is completed through the use of no-reference image fusion metrics including those of information theory based, image feature based, and structural similarity based methods.
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