Fractal Dimension-Based Multi-Focus Image Fusion via Coupled Neural P Systems in NSCT Domain
Liangliang Li, Xiaobin Zhao, Huayi Hou, Xueyu Zhang, Ming Lv, Zhenhong Jia, Hongbing Ma
Abstract
In this paper, we introduce an innovative approach to multi-focus image fusion by leveraging the concepts of fractal dimension and coupled neural P (CNP) systems in nonsubsampled contourlet transform (NSCT) domain. This method is designed to overcome the challenges posed by the limitations of camera lenses and depth-of-field effects, which often prevent all parts of a scene from being simultaneously in focus. Our proposed fusion technique employs CNP systems with a local topology-based fusion model to merge the low-frequency components effectively. Meanwhile, for the high-frequency components, we utilize the spatial frequency and fractal dimension-based focus measure (FDFM) to achieve superior fusion performance. The effectiveness of the method is validated through extensive experiments conducted on three benchmark datasets: Lytro, MFI-WHU, and MFFW. The results demonstrate the superiority of our proposed multi-focus image fusion method, showcasing its potential to significantly enhance image clarity across the entire scene. Our algorithm has achieved advantageous values on metrics QAB/F, QCB, QCV, QE, QFMI, QG, QMI, and QNCIE.