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Objectively adaptive image fusion

机译：客观自适应图像融合

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摘要

Signal-level image fusion has been the focus of considerable research attention in recent years with a plethora of algorithms proposed, using a host of image processing and information fusion techniques. Yet what is an optimal information fusion strategy or spectral decomposition that should precede it for any multi-sensor data cannot be defined a priori. This could be learned by either evaluating fusion algorithms subjectively or indeed through a small number of available objective metrics on a large set of relevant sample data. This is not practical however and is limited in that it provides no guarantee of optimal performance should realistic input conditions be different from the sample data. This paper proposes and examines the viability of a powerful framework for objectively adaptive image fusion that explicitly optimises fusion performance for a broad range of input conditions. The idea is to employ the concepts used in objective image fusion evaluation to optimally adapt the fusion process to the input conditions. Specific focus is on fusion for display, which has broad appeal in a wide range of fusion applications such as night vision, avionics and medical imaging. By integrating objective fusion metrics shown to be subjectively relevant into conventional fusion algorithms the framework is used to adapt fusion parameters to achieve optimal fusion display. The results show that the proposed framework achieves a considerable improvement in both level and robustness of fusion performance on a wide array of multi-sensor images and image sequences.
机译：近年来，信号级图像融合已成为大量研究关注的焦点，其中提出了使用大量图像处理和信息融合技术的大量算法。然而，对于任何多传感器数据，应该在其之前定义的最佳信息融合策略或频谱分解是什么也不可以事先定义。这可以通过主观评估融合算法，或者实际上是通过对大量相关样本数据进行少量的可用客观度量来了解。但是，这是不切实际的，并且受到限制，因为如果实际的输入条件与样本数据不同，它就不能保证最佳性能。本文提出并研究了强大的客观适应性图像融合框架的可行性，该框架可在各种输入条件下显着优化融合性能。想法是采用客观图像融合评估中使用的概念，以使融合过程最佳地适应输入条件。特别关注的是用于显示的融合，它在诸如夜视，航空电子和医学成像等多种融合应用中具有广泛的吸引力。通过将显示出主观相关的客观融合度量集成到常规融合算法中，该框架可用于调整融合参数以实现最佳融合显示。结果表明，所提出的框架在多种多传感器图像和图像序列上的融合性能的水平和鲁棒性上均取得了显着改善。

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