# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 4 Results
### Ablation Study  
In order to better understand our proposed solution, we conducted an ablation study that contains the following situations:  
1. Use super-resolution to only enhance long-exposure test images;
2. Use super-resolution to only enhance middle-exposure test images;
3. Use super-resolution to only enhance short-exposure test images.  
The screenshots of the ablation study results are shown in Figure 10. According to Figure 10,c, by comparing the HDR images' clarity that reconstructed with enhanced medium-exposure image, enhanced long-exposure image, and enhanced short-exposure image, we find that the HDR image reconstructed using the enhanced middle-exposure image has the higher clarity. In Figure 10, by comparing the HDR images' details that reconstructed with enhanced medium-exposure image, enhanced long-exposure image, and enhanced short-exposure image, we find that the HDR image reconstructed using the enhanced middle-exposure image retains fewer details.  
In Table 4, we find that the HDR image, which is reconstructed with enhanced medium-exposure image, decreases PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\). The result of ablation study shows that if we want to retain more details and clarity of the reconstructed HDR image through the super-resolution network, the super-resolution network should be built on the path except the middle-exposure test image.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & & \begin{tabular}{c} **Enhanced** \\ **Lone Exposure** \\ \end{tabular} & \
\begin{tabular}{c} **Enhanced** \\ **Middle Exposure** \\ \end{tabular} &
\begin{tabular}{c} **Enhanced** \\ **Short Exposure** \\ \end{tabular} \\ \hline \multirow{4}{*}{Kalantari} & PSNR-\(\mu\) & 37.700 & 33.874 & 37.738 \\  & SSIM-\(\mu\) & 0.9822 & 0.959 & 0.983 \\ \cline{1-1}  & PSNR-L & 36.213 & 35.001 & 36.080 \\ \cline{1-1}  & SSIM-L & 0.968 & 0.958 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results on public Kalantari datasets [10].  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & & **[PERSON] [8]** & **[PERSON] [10]** & **[PERSON] [11]** & **[PERSON] [14]** & **[PERSON] [32]** & **Ours** \\ \hline \multirow{4}{*}{Kalantari} & PSNR-\(\mu\) & 40.95 & 42.74 & 41.64 & 43.92 & 44.03 \({}^{1}\) & 41.50 \\  & SSIM-\(\mu\) & 0.9805 & 0.9877 & 0.9869 & 0.9905 & 0.9914 \({}^{1}\) & 0.9877 \\  & PSNR-L & 38.31 & 41.22 & 40.91 & 41.57 \({}^{1}\) & 41.18 & 37.84 \\  & SSIM-L & 0.9726 & 0.9848 & 0.9858 & 0.9865 & 0.9871 \({}^{1}\) & 0.9788 \\  & HDR-VDP-2 & 55.72 & 60.51 & 60.50 & 65.45 & 63.02 & 66.05 \({}^{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on public Kalantari datasets [10].  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & & **[PERSON] [8]** & **[PERSON] [10]** & **[PERSON] [11]** & **[PERSON] [14]** & **[PERSON] [32]** & **Ours** \\ \hline \multirow{2}{*}{[PERSON]} & Time(s) & 119.69 & 146.70 & 0.35 & 2.9 & 0.69 & 7.88 \\  & Para(M) & NA & 0.30 & 16.61 & 2.56 & 15.26 & 34.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison the average runtime on public Kalantari datasets [10].