文献类型：会议论文

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

作者：Wang Jinhua;Shi Bing;Feng Songhe

第一作者：王金华

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

第一机构：北京联合大学智慧城市学院

会议论文集：2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)论文集

会议日期：20121021

会议地点：北京

主办单位：中国电子学会;国际自然科学基金委员会;IEEE

语种：英文

中文关键词：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.