文献类型：期刊文献

英文题名：Extreme Learning Machine based exposure fusion for displaying HDR scenes

作者：Wang, Jinhua[1]; Shi, Bing[2]; Feng, Songhe[3]

第一作者：王金华

通讯作者：Wang, J.

机构：[1] Institute of Information Technology, Beijing Union University, Beijing 100101, China; [2] Institute of Humanities and Information, Changchun University of Technology, Changchun 130122, China; [3] Institute of Computer Science and Engineering, Beijing Jiaotong University, Beijing 100044, China

第一机构：北京联合大学智慧城市学院

年份：2012

卷号：2

起止页码：869-872

外文期刊名：International Conference on Signal Processing Proceedings, ICSP

收录：EI(收录号：20131716242969)

语种：英文

外文关键词：Image fusion - Learning systems - Knowledge acquisition

摘要：We know that fusion rule in spatial domain-based 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. ? 2012 IEEE.