激光与光电子学进展, 2019, 56 (10): 101006, 网络出版: 2019-07-04
非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合 下载: 1265次
Image Fusion Based on Fuzzy Logic Combined with Adaptive Pulse Coupled Neural Network in Nonsubsampled Contourlet Transform Domain
图像处理 图像融合 非下采样Contourlet变换 脉冲耦合神经网络 模糊逻辑 边缘特征 image processing image fusion nonsubsampled Contourlet transform pulse coupled neural network fuzzy logic edge feature
摘要
传统的基于多尺度变换的图像融合存在对比度不高、边缘细节等信息保留不理想的问题,为解决此问题,提出了一种基于非下采样Contourlet变换的自适应模糊逻辑和自适应脉冲耦合神经网络(PCNN)的融合算法。对于低频子带方向,采用基于自适应模糊逻辑的融合规则;对于高频子带方向,采用方向信息自适应地调整PCNN的链接强度,以边缘特征作为输入激励自适应PCNN,再根据脉冲点火幅度融合子带系数。实验结果表明,所提融合算法能较好地突出融合图像的目标信息,提供丰富的背景细节,在融合图像的清晰度和人眼视觉方面取得较好的融合效果。
Abstract
Traditional image fusion based on multi-scale transform experiences problems such as low contrast and edge details. A fusion algorithm based on the adaptive fuzzy logic and an adaptive pulse coupled neural network (PCNN) is proposed in the nonsubsampled contourlet transform domain. For the low-frequency sub-band, the fusion is based on the adaptive fuzzy logic. For the high-frequency sub-band, the information about orientation is adaptively utilized as the linking strength of the PCNN and the edge features of the source images are adopted as the input to motivate the adaptive PCNN. Then, the sub-band coefficient is fused according to the pulse ignition amplitude. The experimental results indicate that the proposed fusion algorithm can better highlight the target information of the fusion image, provide richer background details, and achieve a better fusion effect both on the clarity of fusion images and the human vision.
王艳, 杨艳春, 党建武, 王阳萍. 非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合[J]. 激光与光电子学进展, 2019, 56(10): 101006. Yan Wang, Yanchun Yang, Jianwu Dang, Yangping Wang. Image Fusion Based on Fuzzy Logic Combined with Adaptive Pulse Coupled Neural Network in Nonsubsampled Contourlet Transform Domain[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101006.