# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 5 Discussion  
Since using the patch method, the [PERSON] [8] model and the [PERSON] [10] model are not robust for aligning multi-exposure LDR images and have high possibility of causing artifacts in reconstructed HDR images, especially when the foreground of the source LDR images have different poses. [PERSON] [11] model uses homography to align the camera motion and therefore works well in aligning multi-exposure LDR images. However, the [PERSON] [11] model causes the reconstructed HDR image to lose some image details and become a little blurred. The reconstructed HDR images by [PERSON] [14] model HDR-GAN have slight artifacts and are a little blurred. The [PERSON] [32] model works well in processing the alignment of multi-exposure LDR images. However, it will cause the reconstructed HDR images with reduced details on the highlight areas due to long-exposure LDR images with highlights saturated. In contrast, the HDR image reconstructed by our proposed MDHDR model has no artifacts and retains the details that are easily lost in highlight and low-light areas by other models. The main reasons that MDHDR can reconstruct an HDR image well are two. The first one is that mapping the short-exposure image and the long-exposure image to the medium-exposure image, respectively, and then fusing them can effectively avoid the artifacts caused by the different postures of the foreground image. The second one is that the corresponding details of the short-exposure image and the long-exposure image are enhanced in advance in order to solve the blurring of the fused image. Therefore, the fused HDR image can retain details that are closer to the ground truth. It is worth noting that because the corresponding details of the short-exposure image and the long-exposure image are enhanced in advance, the final reconstructed HDR image becomes smoother than the ground truth. Any difference with ground truth image decreases the PSNR-L, PSNR-\(\mu\), SSIM-L, and SSIM-\(\mu\) even though our reconstructed HDR image has better clarity. This is consistent with our expectations and can be observed in Table 2.  
Besides, according to the results of qualitative experiments, we found that our solution has the following two potential limitations. The first one is that when reconstructing HDR  
Figure 10: Ablation study: (**a**) HDR ground truth; (**b**) Use super-resolution to only enhance long-exposure test images; (**c**) Use super-resolution to only enhance middle-exposure test images; (**d**) Use super-resolution to only enhance short-exposure test images; (**e**) Use super-resolution to only enhance long-exposure test images; (**f**) Use super-resolution to only enhance middle-exposure test images; (**g**) Use super-resolution to only enhance short-exposure test images.  
images using multiple exposure images, if the over-bright area appears in the same position in the long-exposure image and the medium-exposure image, it is difficult to use only the image textures of the short-exposure image to restore the image details lost in the over-bright area of the long-exposure image. The second one is that when reconstructing HDR images using multiple-exposure images, if the dark area appears in the same position in the short-exposure image and the medium-exposure image, it is difficult to use only the image textures of the long-exposure image to restore the image details lost in the dark area of the short-exposure image.