红外技术2024，Vol.46Issue(8)：902-911,10.
基于全局能量特征与改进PCNN的红外与可见光图像融合
Infrared and Visible Image Fusion Based on Global Energy Features and Improved PCNN
摘要
Abstract
To improve the low clarity,low contrast,and insufficient texture details of infrared and visible image fusion,an image fusion algorithm based on a parameter-adaptive pulse-coupled neural network(PA-PCNN)was proposed.First,the source infrared image was dehazed by a dark channel to enhance the clarity of the image.Then,the source images were decomposed by non-subsampled shearlet transform(NSST),and the low-frequency coefficients were fused by the proposed global energy feature extraction algorithm combined with a modified spatial frequency adaptive weight.Texture energy was used as the external input of the PA-PCNN to fuse the high-frequency coefficients,and the fused gray image was obtained using the inverse NSST.To further enhance the perception of the human eye,a multiresolution color transfer algorithm was used to convert the grayscale image to a color image.The proposed method was compared with seven classical algorithms for two image pairs.The experimental results show that the proposed method is significantly better than the comparison algorithms in terms of evaluation indicators,and improves the clarity and detail information of the fused image,which verifies its effectiveness.The conversion of the fused grayscale images into pseudo-color images further enhances recognition and human eye perception.关键词
图像融合/非下采样剪切波变换/全局能量特征/纹理能量/脉冲耦合神经网络Key words
image fusion/non-subsampled shearlet transform/global energy features/texture energy/pulse-coupled neural network分类
计算机与自动化引用本文复制引用
邢延超,牛振华..基于全局能量特征与改进PCNN的红外与可见光图像融合[J].红外技术,2024,46(8):902-911,10.基金项目
山东省自然科学基金(ZR2021MF101). （ZR2021MF101）
