Unmanned aerial vehicles, with their inherent fine attributes, such as flexibility, mobility, and autonomy, play an increasingly important role in the Internet of Things (IoT). Airborne infrared and visible image fusion, which constitutes an important data basis for the perception layer of IoT, has been widely used in various fields such as electric power inspection, military reconnaissance, emergency rescue, and traffic management. However, traditional infrared and visible image fusion methods suffer from weak detail resolution. In order to better preserve useful information from source images and produce a more informative image for human observation or unmanned aerial vehicle vision tasks, a novel fusion method based on discrete cosine transform (DCT) and anisotropic diffusion is proposed. First, the infrared and visible images are denoised by using DCT. Second, anisotropic diffusion is applied to the denoised infrared and visible images to obtain the detail and base layers. Third, the base layers are fused by using weighted averaging, and the detail layers are fused by using the Karhunen–Loeve transform, respectively. Finally, the fused image is reconstructed through the linear superposition of the base layer and detail layer. Compared with six other typical fusion methods, the proposed approach shows better fusion performance in both objective and subjective evaluations.

中文翻译：

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基于DCT和各向异性扩散的无人机在未来5G IoT场景中的异构图像融合方法

无人驾驶飞机具有其固有的优良属性，例如灵活性，移动性和自治性，在物联网（IoT）中扮演着越来越重要的角色。机载红外和可见光图像融合是物联网感知层的重要数据基础，已被广泛用于电力检查，军事侦察，紧急救援和交通管理等各个领域。但是，传统的红外和可见图像融合方法的细节分辨率较弱。为了更好地保存源图像中的有用信息，并为人类观察或无人机视觉任务提供更多信息的图像，提出了一种基于离散余弦变换（DCT）和各向异性扩散的新型融合方法。第一，使用DCT对红外图像和可见图像进行去噪。其次，将各向异性扩散应用于去噪的红外和可见图像，以获得细节层和基础层。第三，分别通过使用加权平均来融合基础层，并通过使用Karhunen-Loeve变换来融合细节层。最后，通过基础层和细节层的线性叠加来重建融合图像。与其他六种典型融合方法相比，该方法在客观和主观评估中均显示出更好的融合性能。分别。最后，通过基础层和细节层的线性叠加来重建融合图像。与其他六种典型融合方法相比，该方法在客观和主观评估中均显示出更好的融合性能。分别。最后，通过基础层和细节层的线性叠加来重建融合图像。与其他六种典型融合方法相比，该方法在客观和主观评估中均显示出更好的融合性能。