Multi-Scale Ensemble Learning for Thermal Image Enhancement release_2522wjrkabffbfkqf44n4vdh7u

by Yuseok Ban, Kyungjae Lee

Published in Applied Sciences by MDPI AG.

2021   Volume 11, p2810

Abstract

In this study, we propose a multi-scale ensemble learning method for thermal image enhancement in different image scale conditions based on convolutional neural networks. Incorporating the multiple scales of thermal images has been a tricky task so that methods have been individually trained and evaluated for each scale. However, this leads to the limitation that a network properly operates on a specific scale. To address this issue, a novel parallel architecture leveraging the confidence maps of multiple scales have been introduced to train a network that operates well in varying scale conditions. The experimental results show that our proposed method outperforms the conventional thermal image enhancement methods. The evaluation is presented both quantitatively and qualitatively.
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Date   2021-03-22
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