Abstract

Abstract In this paper, targeting image translation between the thermal and visible domains, we propose a novel framework to enhance the edge and boundary features during translation. We tackle the unsupervised training task where sample image pairs from two domains are randomly chosen so the image content does not match, but we also take advantage of paired feature maps during the feature disentanglement process. This can be considered to be weakly-supervisedtraining. First, since thermal images usually have vague edges, we propose to apply a Canny operator to strengthen the edge features of the thermal images. Next, to define the correct object boundaries, we extract the boundary features from silhouette masks which are paired with the visible domain images. Then we disentangle the features of both domains into a domain-shared latent space and a domain-exclusive latent space. In the domain-shared latent space, the edge and boundary features extracted from the thermal and visible domains respectively act as domain-shared information which is used to render the edges and define the boundaries of the translated images. In the domain-exclusive latent space, domain-exclusive information such as colour is used to render the colour of objects of the translated images. In addition, we propose a pixel-wise adversarial loss rather than more traditional ones. The experimental results show that the proposed method has the ability to render realistic edge and colour features within the correct object boundaries and outperforms several state-of-the-art methods.

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