# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 4 Results  
### Qualitative  
To validate our hypotheses, we conduct qualitative tests on MDHDR and other state-of-the-art networks. In the qualitative comparison, we use different datasets, such as [PERSON] dataset [10], [PERSON] dataset [38], and [PERSON] dataset [44], to compare the HDR images generated by MDHDR and other state-of-the-art models in terms of image alignment and image details.  
In Figure 7, we focus on the results of the foreground and background images of the reconstructed HDR image. In the foreground part, all models can align LDR images well to generate HDR images. However, in terms of background part, the models of [PERSON] [8], [PERSON] [10], and [PERSON] [14] show serious artifacts. In addition, in the area of tree shadows, the HDR images reconstructed by [PERSON] [11] and our MDHDR retain more image details and no serious ghosting artifacts.  
Figure 7: Results of various models on HDR images reconstructed by using the [PERSON] dataset [10]: (**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.  
Because the Tursen dataset has no ground truth HDR image, we use the free version of Photomatix tool to generate the tone-mapped HDR image as HDR GT in Figure 8[39]. In the high-brightness background part, [PERSON] [8] and [PERSON] [10] models produce obvious artifacts. Our MDHDR adaptors are the same as [PERSON] [32] model that fuse images majorly basing on the medium-exposure image component and results in that the wall and the tree have similar colors, but our result has more details than the [PERSON] [32] model. In the low-brightness foreground part of the reconstructed HDR image, most models perform well in alignment, even though the multi-exposure LDR images have different poses. In the medium-brightness foreground part of the reconstructed HDR image, the [PERSON] [8] and [PERSON] [10] models produced artifacts; other models reconstructed HDR images well. It is worth to note that the result of comparing with other models, our MDHDR can reconstruct an HDR image that is clearer and has more details.  
In Figure 9, [PERSON] [8] and [PERSON] [10] produce artifacts in the high-brightness background. [PERSON] [11] model retains the details of image objects with lower brightness but loses the details in the high-brightness area. [PERSON] [14] model loses the low-contrast details of the image. Our MDHDR adaptors are the same as [PERSON] [32] model that fuses images majorly basing on the medium-exposure image component and results in reconstructed HDR image similar to medium-exposure LDR image, but our MDHDR retains more details in the high-brightness contrast image than [PERSON] [32] model. In Figure 9, all models perform well in alignment in low-brightness areas. It is worth noting that our MDHDR significantly eliminates noises in low-light areas. According to Figures 7-9, the HDR image reconstructed by our proposed MDHDR achieves not only having no ghosts, retaining the image details in the highlight and low-light areas, but also reducing the image noises in the low-light area and having a better clarity than the ground truth image.