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首页> 外文期刊>IEEE Transactions on Image Processing >A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion
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A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion

机译：具有数据驱动层集成模型的双模集成模型，用于多模态图像融合

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摘要

Image fusion plays a critical role in a variety of vision and learning applications. Current fusion approaches are designed to characterize source images, focusing on a certain type of fusion task while limited in a wide scenario. Moreover, other fusion strategies (i.e., weighted averaging, choose-max) cannot undertake the challenging fusion tasks, which furthermore leads to undesirable artifacts facilely emerged in their fused results. In this paper, we propose a generic image fusion method with a bilevel optimization paradigm, targeting on multi-modality image fusion tasks. Corresponding alternation optimization is conducted on certain components decoupled from source images. Via adaptive integration weight maps, we are able to get the flexible fusion strategy across multi-modality images. We successfully applied it to three types of image fusion tasks, including infrared and visible, computed tomography and magnetic resonance imaging, and magnetic resonance imaging and single-photon emission computed tomography image fusion. Results highlight the performance and versatility of our approach from both quantitative and qualitative aspects.
机译：图像融合在各种视觉和学习应用中起着关键作用。目前的融合方法旨在表征源图像，专注于某种类型的融合任务，而在广泛的方案中有限。此外，其他融合策略（即加权平均，选择 - 最大）无法承担具有挑战性的融合任务，这进一步导致不良文物在其融合结果中出现。在本文中，我们提出了一种具有彼此优化范例的通用图像融合方法，针对多模态图像融合任务。在从源图像分离的某些组件上进行相应的交替优化。通过自适应集成权重映射，我们能够在多模态图像中获得灵活的融合策略。我们成功地将其应用于三种类型的图像融合任务，包括红外和可见，计算机断层扫描和磁共振成像，以及磁共振成像和单光子发射计算机断层摄影图像融合。结果突出了我们从定量和定性方面的方法的性能和多功能性。

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