文献类型：会议论文

英文题名：Extreme Learning Machine Based Exposure Fusion for Displaying HDR Scenes

作者：Wang Jinhua[1];Shi Bing[2];Feng Songhe

第一作者：王金华

通讯作者：Wang, JH[1]

机构：[1]Beijing Union Univ, Inst Informat Technol, Beijing 100101, Peoples R China;[2]Changchun Univ Technol, Inst Human Informat, Changchun 130122, Peoples R China; Beijing Jiaotong Univ, Inst Comp Sci Engn, Beijing 100044, Peoples R China

第一机构：北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Inst Informat Technol, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;

会议论文集：IEEE 11th International Conference on Signal Processing (ICSP)

会议日期：OCT 21-25, 2012

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：Exposure Fusion; High Dynamic Range; Extreme Learning Machine; Fusion Rule

摘要：We know that fusion rule in spatial domainbased multiple exposure fusion methods, the sum weighted average is usually used in which same weight value is assigned for each source image, regardless of the details contained in it. Furthermore, using only single feature to design the fusion rule is also commonly adopted. However, utilizing single feature to measure the quality of one image is not comprehensive. As a result, the detail losing and contrast reduction are caused by these rules. In the paper, In order to use multiple features extracted from one image simultaneously to obtain an adaptive weight value for the image, we propose an exposure fusion method called (ELM_EF). It is based on a regression method called Extreme Learning Machine (ELM). Firstly, we construct input vector for ELM using contrast, saturation and exposedness features from the chosen representative blocks. The label of input is obtained by using a Gaussian function with exposure setting of the image served as a parameter. Thus, training model can be got. Secondly, the statistic values of these features about each tested image are calculated, it is used for deciding the weight value of corresponding image with the training model. Experiments show that the proposed method can preserve more details and contrast than the sum weighted average method. Moreover, it can give comparative or even better results compared to other typical exposure fusion methods.