# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 4 Results
### Quantitative  
To be objective, we conduct quantitative testing regarding MDHDR and other advanced models after qualitative experiments. In the quantitative comparison, we use the Kalantari dataset [10] to test all models and calculate their PSNR-L, SSIM-L, PSNR-\(\mu\), SSIM-\(\mu\), and HDR-VDP-2 [51]. The result of the quantitative testing is recorded in Table 2.  
Table 2 focuses on PSNR-L, PSNR-\(\mu\), SSIM-L, SSIM-\(\mu\), and HDR-VDP2. The higher the values of PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\) mean the reconstructed HDR image is more similar to the ground truth. The higher the HDR-VDP2 value mean the visual quality of the reconstructed HDR image is better. In general, the main reason for the decrease of PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\) is the image artifacts and loss of details; the reason for the decrease of HDR-VDP2 value is the image becomes blurred. Since the architecture of our proposed solution is based on [PERSON] [32], we directly compare the results of our solution with [PERSON] [32] results. Table 2 shows that our solution has lower PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\) than [PERSON] [32], which means that the HDR image reconstructed by our solution has more difference from the ground truth. Table 2 shows that our solution gets higher HDR-VDP-2 score than that of [PERSON] [32], which means the HDR image, which is reconstructed by our solution, is better in visual perception.  
In addition, the results of the qualitative experiments in Figures 7-9 confirm that the HDR images, which are reconstructed by our solution, do not produce obvious artifacts or loss of details and have better clarity than the ground truth. Therefore, for the decrease in PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\) but the increase in HDR-VDP-2, our interpretation is that the improvement in image clarity leads to a gap with the ground truth. This result is consistent with our expectations. In other words, Table 2 confirms that our proposed HDRI reconstruction solution can avoid artifacts, detail loss, and image blur simultaneously.  
We also list the running time and parameter size of models, as shown in Table 3. Our testing environment uses windows10 OS, IS-4570 CPU, and one GTX-1070 Ti. The codes of Sen [8] and Kalantari [10] were performed by the CPU. Others were performed by the GPU.  
Figure 8: Results of various models on HDR images reconstructed by using the [PERSON] dataset [38]: (**a**) the patch of input multi-exposure LDR images; (**b**) the patch of input multi-exposure LDR images; (**c**) the ground truth HDR image; (**d**) result of [PERSON] [8] model; (**e**) result of [PERSON] [10] model; (**f**) result of [PERSON] [11] model; (**g**) result of [PERSON] [14] model; (**h**) result of [PERSON] [32] model; (**i**) result of Ours; (**j**) the ground truth HDR image.  
Figure 9: Results of various models on HDR images reconstructed by using the [PERSON] dataset [44]: (a) the patch of input multi-exposure LDR images; (b) the patch of input multi-exposure LDR images; (c) the ground truth HDR image; (d) result of [PERSON] [8] model; (e) result of [PERSON] [10] model; (f) result of [PERSON] [11] model; (g) result of [PERSON] [14] model; (h) result of [PERSON] [32] model; (i) result of Ours; (j) the ground truth HDR image.  
In Table 3, the focus is on the network size and the inference time. Our proposed solution is based on Lee [32] and super-resolution [28], which has better HDR image quality but has more parameters, which costs the expense of long inference time. The large amount of Para (M) and inference time prompts us to think that simplifying super-resolution models will be the major task in our future research.