文献类型：期刊文献

英文题名：Exposure fusion based on sparse representation using approximate K-SVD

作者：Wang, Jinhua[1,2];Liu, Hongzhe[1];He, Ning[2]

第一作者：王金华

通讯作者：Wang, JH[1]

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

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll 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;

年份：2014

卷号：135

起止页码：145-154

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20141517555569);Scopus(收录号：2-s2.0-84897912908);WOS:【SCI-EXPANDED(收录号:WOS:000335871200018)】；

基金：This work is partly supported by the National Nature Science Foundation of China (No. 61202245, No. 61271370, No. 61271369, No. 61372148, No. 61370138), and the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (CIT&TCD20130513).

语种：英文

外文关键词：Exposure fusion; High dynamic range imaging; Sparse representation; OMP; Approximate K-SVD

摘要：In this paper, we propose a novel exposure fusion scheme using the sparse representation theory, which can explore the sparseness of the source images. First, we present a novel way to get the chrominance information of the scene, and the saturation of the fused image can be adjusted using one user-controlled parameter. Second, we conduct the sparse representation on overlapping patches of luminance images obtained by 'sliding window technique', which use dictionary obtained by K-SVD with typical indoor and outdoor multiple exposure sequences. In addition, we introduce an efficient implementation of K-SVD (called approximate K-SVD) which can reduce complexity as well as memory requirements. Third, the coefficients are combined with a novel "frequency of atoms usage" fusion rule strategy. Finally, the fused image is reconstructed from the combined sparse coefficients and the used dictionary. Experiments show that the proposed method can give comparative results compared to state-of-art exposure fusion methods. (C) 2014 Elsevier B.V. All rights reserved.