Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Yang, Yong; Que, Yue; Huang, Shu-Ying; Lin, Pan
• Is Part Of: Signal, image and video processing. Volume 11:Issue 3(2017)
• Page Start: 439
• Page End: 446
• Publication Date: 2017-03
• Subjects: Image fusion -- Fuzzy logic -- Nonsubsampled contourlet transform -- Sum modified-laplacian -- Adaptive pulse-coupled neural network; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0979-1
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Multi-focus image fusion technique can solve the problem that not all the targets in an image are clear in case of imaging in the same scene. In this paper, a novel multi-focus image fusion technique is presented, which is developed by using the nonsubsampled contourlet transform (NSCT) and a proposed fuzzy logic based adaptive pulse-coupled neural network (PCNN) model. In our method, sum-modified Laplacian (SML) is calculated as the motivation for PCNN neurons in NSCT domain. Since the linking strength plays an important role in PCNN, we propose an adaptively fuzzy way to determine it by computing each coefficient's importance relative to the surrounding coefficients. Combined with human visual perception characteristics, the fuzzy membership value is employed to automatically achieve the degree of importance of each coefficient, which is utilized as the linking strength in PCNN model. Experimental results on simulated and real multi-focus images show that the proposed technique has a superior performance to series of exist fusion methods.