Comparative Analysis of Image Fusion Methods in Marine Environment

Image fusion methods have gained a lot of attraction over the past few years in the field of sensor fusion. An efficient image fusion approach can obtain complementary information from various multi-modality images. In addition, the fused image is more robust to imperfect conditions such as mis-registration and noise. The aim of this paper is to explore the performance of existing deep learning-based and traditional image fusion techniques for our real marine images. The performance of these techniques is evaluated with six common quality metrics. Image data was collected using a sensor system onboard a vessel in the Finnish archipelago. This system is used for developing autonomous vessels, and records data in a range of operation and climatic conditions. To the best of our knowledge, there is not a comparative study of RGB and infrared image fusion algorithms evaluated in a marine environment. Experimental results indicate that deep learning-based fusion methods can significantly improve the image fusion performance considering both the visual quality and objective assessment comparison against with other methods.