A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios

Abstract

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.

FSADFuse: A Novel Fusion Approach to Infrared and Visible Images

“…( ) S  is an edge stop function that controls the intensity of diffusion. Literature [26], [28] further proposed the following two Equations:…”

“…Furthermore, Vishwakarma et al [25] proposed a fusion approach based on adjustable nonsubsampled shearlet transform, it takes into account the structure information of the image at different scales and has a good fusion effect. Hao et al [26] proposed an algorithm based on the discrete cosine transform and anisotropic diffusion, and this algorithm preserves more useful information from the source image. Moreover, Harbinder et al [27] introduced anisotropic diffusion for image fusion.…”