文献类型：期刊文献

英文题名：Exposure Fusion Using a Relative Generative Adversarial Network

作者：Wang, Jinhua[1];Li, Xuewei[2];Liu, Hongzhe[2]

通讯作者：Li, XW[1]

机构：[1]Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学继续教育学院

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2021

卷号：E104D

期号：7

起止页码：1017-1027

外文期刊名：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

收录：;EI(收录号：20212910640888);Scopus(收录号：2-s2.0-85109609437);WOS:【SCI-EXPANDED(收录号:WOS:000680156600010)】；

基金：This work was supported by National Nature Science Foundation of China (No.61572077, No.61872042, No.61871039), the projects (KM202111417009, KZ201911417048) supported by the Education Commission of Beijing Municipal, the Collaborative Innovation Center for Visual Intelligence (Grant No. CYXC2011), the Academic Research Projects of Beijing Union University (No. ZB10202003), Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2020AZ01), and the Academic Research Projects of Beijing Union University (No.ZK50202001).

语种：英文

外文关键词：exposure fusion; relative generative adversarial network; high dynamic range image

摘要：At present, the generative adversarial network (GAN) plays an important role in learning tasks. The basic idea of a GAN is to train the discriminator and generator simultaneously. A GAN-based inverse tone mapping method can generate high dynamic range (HDR) images corresponding to a scene according to multiple image sequences of a scene with different exposures. However, subsequent tone mapping algorithm processing is needed to display it on a general device. This paper proposes an end-to-end multi-exposure image fusion algorithm based on a relative GAN (called RaGAN-EF), which can fuse multiple image sequences with different exposures directly to generate a high-quality image that can be displayed on a general device without further processing. The RaGAN is used to design the loss function, which can retain more details in the source images. In addition, the number of input image sequences of multi-exposure image fusion algorithms is often uncertain, which limits the application of many existing GANs. This paper proposes a convolutional layer with weights shared between channels, which can solve the problem of variable input length. Experimental results demonstrate that the proposed method performs better in terms of both objective evaluation and visual quality.