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 thermal image super-resolution, via integrating high-frequency information from the visual image. (II) Investigation of different network architecture schemes in the literature, their up-sampling methods, learning procedures, and their optimization functions by showing their beneficial contribution to the super-resolution problem. (III) A benchmark ULB17-VT dataset that contains thermal images and their visual images counterpart is presented. (IV) Presentation of a qualitative evaluation of a large test set with 58 samples and 22 raters which shows that our proposed model performs better against state-of-the-arts.
CITATION STYLE
Almasri, F., & Debeir, O. (2019). Multimodal Sensor Fusion in Single Thermal Image Super-Resolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11367 LNCS, pp. 418–433). Springer Verlag. https://doi.org/10.1007/978-3-030-21074-8_34
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