# Multi-Scale Ensemble Learning for Thermal Image Enhancement
## 4 Experiments and Discussion
### Comparative Studies  
We compared the representative network with the three-level sub-networks to the bicubic method and some other state-of-the-art methods: TEN [10], TIECNN [11], ASR2_s [15], and TIR-DCSCN [12]. The evaluation was done on the test dataset, while the PSNR and SSIM against the ground truth were used as the evaluation metrics. In addition, line profiling [15] was conducted for the in-depth analysis of the pixel values on a reference line. Table 5 quantitatively compares the results of different methods on the test dataset. The compared methods had the limitations that each scale needed an individual network trained according to the specific scale. For example, TEN [10] (\(\times 3\)) performed better at a scale of three than the bicubic method did, while its performance rather degraded at a scale of two as it was trained for the specific scale. TIECNN [11] (\(\times 2\)) showed better performance in terms of both the PSNR and SSIM compared to the bicubic method at all test scales. However, note that the PSNR results of TIECNN [11] (\(\times 3\)) and (\(\times 4\)) were lower than those of the bicubic method at other scales. On the other hand, it should be pointed out that our method as a single network outperformed the other compared methods at all scales. We show that a single network can be effectually trained to deal with different scale conditions. Figures 11-13 qualitatively compares the results of different methods with a scale factor of four. Two specific regions in an image are enlarged to effectively show and emphasize the enhanced details. It is worth noting that the results of our network are perceptually better, revealing more details on object boundaries. That is to say, the edges from a low-quality input image are effectively reconstructed to be of higher quality. Moreover, the texture around an edge is well preserved while enhancing the low-quality thermal image. When observing the red colored and blue-colored boxes in Figure 11, the result of our proposed method looks clear and resembles the ground truth, while the other methods are relatively blurry. As presented in Figure 12, our proposed method better minimizes the deformation  
Figure 10: Results of our proposed network (left: low-quality input image; right: enhanced output image). of a regional detail (e.g., the black regions in the red-colored box) and produces clear edges (e.g., the horizontal boundary in the blue-colored box). Our method also fairly preserves the texture information, i.e., temperature value, at and around the pixels where the details are restored. Compared to the noisy or blurry edges obtained by the compared methods, our method recovers as much detail as the ground truth image has (see Figure 13). Figure 12: Qualitative comparison on the test dataset ([29]-T138-No.28). Lastly, to analyze the pixel values of the enhanced thermal images, we performed line profiling [15] on the test images with a scale factor of four. This metric was used to directly analyze and compare the thermal information to the ground truth. To evaluate a line profile in our experiment, firstly, horizontal or vertical lines were set, as shown in Figure 14 (green-colored line in the left column). Then, the absolute difference between the pixel values of the ground truth and those of the enhanced image on the line was calculated. Finally, the mean and standard deviation were computed using the values of the absolute difference. As shown in Figure 14 (right column), it can be observed that our method is more similar to the ground truth than other methods. In particular, within the positions on the line from 160 to 170 on Line 3 in Figure 14, it can be seen that the curves of the other methods have a smoother slope compared to the ground truth, whereas our method shows the most similar curve to the ground truth. Table 6 presents the comparison of the mean and standard deviation of the absolute value difference between the ground truth and each method on Lines 1, 2, and 3 in Figure 14, respectively.