基于PCANet的非下采样剪切波域多聚焦图像融合  

Multi-focus Image Fusion Method Based on PCANet in NSST Domain

作　　者：黄晓生[1] 徐静 HUANG Xiao-sheng;XU Jing(School of Software Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学软件学院,南昌330013

出　　处：《计算机科学》2021年第9期181-186,共6页Computer Science

基　　金：国家自然科学基金(61763011,61762037,61962021);江西省自然科学基金(20192BBE50079)。

摘　　要：近年来,基于深度学习模型的图像融合方法备受关注。而传统的深度学习模型通常需要耗时长和复杂的训练过程,并且涉及参数众多。针对这些问题,文中提出了一种基于简单的深度学习模型PCANet的非下采样剪切波(Non-Subsanmpled Shearlet Transform,NSST)域多聚焦图像融合方法。首先,利用多聚焦图像训练两阶段PCANet,用于提取图像特征。然后,对输入源图像进行NSST分解,得到源图像的多尺度和多方向表示。低频子带利用训练好的PCANet提取其图像特征,并利用核范数构造有效的特征空间进行图像融合。高频子带利用区域能量取大的融合规则进行融合。最后对根据不同融合规则融合后的频率系数进行NSST重构,获取清晰的目标图像。实验结果表明,所提算法的训练和融合速度比基于CNN的方法提高了43%,该算法的平均梯度、空间频率、熵等融合性能分别为5.744,15.560和7.059,可以与现有融合方法相媲美或优于现有的融合方法。The deep learning model based image fusion methods have attracted much attention in recently years.But the traditio-nal deep learning model usually needs a time-consuming and complex training process and a difficulty parameters tuning process on large datasets.To overcome these problems,a simple deep learning model PCANet based multi-focus image fusion method in NSST domain is proposed.Firstly,multi-focus images are used to train two-stage PCANet to extract image features.Then,the input source image is decomposed by NSST to obtain the multi-scale and multi-directional representation of the source image.The low frequency subband uses the trained PCANet to extract its image features,and uses the kernel norm to construct an effective feature space for image fusion.High frequency subbands are fused using the fusion rule of regional energy maximization.Finally,the frequency coefficients fused according to different fusion rules are reconstructed by NSST to obtain a clear target image.The experimental results show that the training and fusion speed of the algorithm is 43%higher than that of the CNN-based method.The average gradient,spatial frequency and entropy of the proposed algorithm are 5.744,15.560 and 7.059 respectively,which can be comparable to or superior to the existing fusion methods.

关 键 词：多聚焦图像融合 深度学习 PCANet NSST CNN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]