Image fusion algorithm based on gradient domain guided filtering and improved PCNN

被引：0

作者：

Wang J. ^{[1
,2
]}

He Z. ^{[1
]}

Liu J. ^{[1
]}

Yang K. ^{[1
]}

机构：

[1] Electronic and Information College, Northwestern Polytechnical University, Xi'an

[2] No.365 Institute, Northwestern Polytechnical University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 08期

关键词：

guided filtering; image fusion; improved pulse-coupled neural network (PCNN);

D O I：

10.12305/j.issn.1001-506X.2022.08.01

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to solve the problems of halo artefacts and unfavourable visual perception in the fused images, this paper proposes an image fusion algorithm based on gradient-domain guided filtering and an improved pulse-coupled neural network (PCNN). First, an image fusion model is constructed using the image features of image structure, sharpness and contrast saliency. Secondly, the initial decision map is optimised by inter-pixel correlation using gradient-domain guided filtering instead of the traditional optimisation method. Then, the optimised decision map is used as external input to stimulate the improved PCNN model to obtain the fusion weight map. Finally, the source image and the fusion weight map are weighted to obtain the final Finally, the source image and the fusion weight map are weighted to obtain the final fused image. The experimental results show that this method can better preserve the image edge, texture and detail information, avoid halo artefacts on the target edge, and facilitate visual observation. © 2022 Chinese Institute of Electronics. All rights reserved.

引用

页码：2381 / 2392

页数：11

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