# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 1 Introduction  
General purpose photography equipment on the market cannot preserve the full dynamic range of a real scene within a single frame [1]. Reconstructed HDR images can make the images present a dynamic range closer to the real scene. From the development of HDR image reconstruction, many HDR image datasets have been established by scholars as the basis for result evaluation, and these datasets also provide momentum for continuous progress in this field. Scholars provide a lot of various methods for reconstructing HDR images, including alignment-based [2, 3, 4], rejection-based [5, 6, 7], patch-based [8, 9], and CNN-based [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. With the help of the rapid development of deep learning, the results of reconstructing HDR images have made great progress. However, the work of reconstructing HDR images still faces challenges. The quality of reconstructed HDR images is prone to defects, such as artifacts, loss of details, and image blur. We believe a good solution for reconstructing HDR images must have the following capabilities.  
The purpose of this paper is to propose a method to avoid the three major problems of artifacts, loss of details, and image blur when reconstructing HDRI. We assume that to solve the above three problems to reconstruct HDR images, several capabilities listed below are required in the solution:  
* Ability to enhance image details;
* Ability to extract image features;
* Ability to accurately align the poses of objects in images with different exposures;
* Ability to accurately fuse multiple exposure images.  
In order to solve the above three problems, we proposed a solution called more detail HDR (MDHDR) to reconstruct HDR images. MDHDR is based on inspiration from manytechnologies, such as fusing multi-exposure images and super-resolution [8, 11, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]. The MDHDR solution integrates the network architecture in paper [28, 32] and is divided into three main functional blocks. The first block is to enhance the details of long-exposure and short-exposure LDR images. This part adopts the network architecture of paper [28]. The second functional block is responsible for capturing the image details of the low-exposure highlight area and the high-exposure low-light area, respectively, and fusing them into the medium-exposure image. This part adopts the network architecture of paper [30, 31, 32]. The third block uses a residual network to perform fusion on the long- and short-exposure images and the medium-exposure image after detail enhancement. This part mainly adopts the network architecture of papers [26, 32, 34]. Based on the objective conditions of using a public dataset, the experimental results confirm that the HDR images reconstructed by our proposed MDHDR solution are extremely competitive in terms of image clarity. The main difference between MDHDR and network in the paper [32] is that MDHDR uses a super-resolution network to enhance the details of long-exposure and short-exposure images, compensating for the loss of details in the HDR image reconstruction process, thereby reconstructing a better-quality HDR image.