Multimodal Sensor Fusion In Single Thermal image Super-Resolution

Almasri, Feras, Debeir, Olivier

arXiv.org Machine Learning 

With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary in a large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a deep learning solution to enhance the thermal image resolution. The following results are given: (I) Introduction of a multimodal, visual-thermal fusion model that addresses thermalimage super-resolution, via integrating high-frequency information from the visual image.

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