文献类型：会议论文

英文题名：Exposure fusion based on support vector regression

作者：Wang, Jinhua[1]; Bao, Hong[2]; He, Ning[1]

第一作者：王金华

机构：[1] Institute of Information Technology, Beijing Union University, Beijing, 100101, China; [2] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学智慧城市学院

会议论文集：ICIMCS 2014 - Proceedings of the 6th International Conference on Internet Multimedia Computing and Service

会议日期：July 10, 2014 - July 12, 2014

会议地点：Xiamen, China

语种：英文

外文关键词：Computation theory - Support vector regression

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 (SVR-EF). It is based on Support Vector Regression (SVR) theory. Firstly, we construct input vector for SVR 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. Copyright 2014 ACM.