title: Effective multifocus image fusion based on HVS and BP neural network.
abstract: The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations.
authors.affiliation: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China ; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China.
authors.forename: Wenjuan
authors.identifier: 0000-0002-6713-7082
authors.initials: W
authors.lastname: Zheng
authors.affiliation: School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.
authors.forename: Shuying
authors.identifier: 0000-0003-2771-8461
authors.initials: S
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D032701:Biomimetics / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique; D014796:Visual Perception