The fusion of multiple images from different modalities is the process of generating a single output image that combines the useful information of all input images. Ideally, the information-rich content of each input image would be preserved, and the cognitive effort required by the user to extract this information should be smaller on the fused image than the one required to examine all images. We propose MobileFuse, an edge computing method targeted at processing large amount of imagery in a bandwidth limited environment using depthwise separable Deep Neural Networks (DNNs). The proposed approach is a hybrid between generative and blending based methods. Our approach can be applied in various fields which require low latency interaction with the user or with an autonomous system. The main challenge in training DNNs for image fusion is the sparsity of data with representative ground truth. Registering images from different sensors is a major challenge in itself, and generating a ground truth from them is another massive one. For this reason, we also propose a multi-focus and multi-lighting framework to generate training dataset using unregistered images. We show that our edge network can perform faster than its state-of-the-art baseline, while improving the fusion quality.

Multimodal image registration is a challenging task. To begin with, the variation of parallax in the images makes the process intrinsically tricky. Additionally, due to phenomenology differences in modalities, the appearance of the same feature may vary significantly between the images making the registration laborious. To help mitigate these issues, we propose a two-step approach targeted at visible and infrared imagery. First, we train a generative adversarial network to learn the domain transfer function between the visible and the infrared domain, thereby mitigating the impact of the visual dissimilarity between the images. Second, we train a deep Siamese network to compute a homography in an unsupervised setting. Both elements are combined and trained sequentially. Our method is evaluated on a publicly available dataset. Our results show that the proposed method provides a reduction of more than 30% on average from the previous state-of-the-art, and outperforms several baselines and recent deep homography methods.