Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network

• Published in: Signal, image and video processing image and video processing, 2017-03, Vol.11 (3), p.439-446
• Main Authors: Yang, Yong, Que, Yue, Huang, Shu-Ying, Lin, Pan
• Format: Article
• Language: English
• Subjects: Coefficients; Computer Imaging; Computer Science; Computer vision; Fuzzy logic; Image processing; Image Processing and Computer Vision; Multimedia Information Systems; Neural networks; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Visual perception
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-016-0979-1

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.