# Multi-Scale Ensemble Learning for Thermal Image Enhancement
## 5 Conclusions  
The objective of this study was to enhance the low quality of a thermal image regardless of scale variation based on a single network. To achieve this objective, we introduced a novel parallel architecture leveraging the confidence maps of multiple levels. Our approach includes the mixed feature module that enhances connectivity among sub-networks and the combination module that is incorporated with the residual and confidence values. Through ablation studies, it was shown that each module was effective in enhancing the quality of a thermal image. Our method not only outperformed the comparing methods in the quantitative evaluation, but also produced perceptually better results when visually compared. In this study, we show that the networks for an individual scale can be integrated by our proposed architecture as a single network. [PERSON] and [PERSON] conceived of and designed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON] performed the experiments. All authors read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1G1A1102041). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Line 1**} & \multicolumn{2}{c}{**Line 2**} & \multicolumn{2}{c}{**Line 3**} \\ \cline{2-7}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline Bicubic & 4.543 & 4.684 & 5.824 & 6.144 & 4.311 & 9.219 \\ TEN [10] & 4.391 & 4.178 & 5.847 & 5.606 & 4.436 & 7.867 \\ TIECNN [11] & 3.874 & 4.004 & 4.939 & 4.557 & 2.573 & 4.512 \\ ASR2\_s [15] & 4.013 & 4.051 & 4.496 & 4.602 & 3.017 & 7.294 \\ TIR-DCSCN [12] & 4.278 & 4.505 & 5.695 & 5.776 & 4.390 & 8.794 \\ Ours & 3.570 & 3.584 & 4.351 & 4.076 & 2.274 & 4.332 \\ \hline \hline \end{tabular}
\end{table} 
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