Multifocus image fusion using convolutional neural network

被引：13

作者：

Wen, Yu ^{[1
]}

Yang, Xiaomin ^{[1
]}

Celik, Turgay ^{[2
]}

Sushkova, Olga ^{[3
]}

Albertini, Marcelo Keese ^{[4
]}

机构：

[1] Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China

[2] Univ Witwatersrand, Sch Comp Sci & Appl Math, Johannesburg, South Africa

[3] Kotelnikov Inst Radio Engn & Elect, Moscow, Russia

[4] Univ Fed Uberlandia, Dept Comp Sci, Uberlandia, MG, Brazil

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2020年 / 79卷 / 45-46期

基金：

中国国家自然科学基金;

关键词：

Image fusion; Multi-focus; Convolutional neural network; Morphological filtering; PERFORMANCE;

D O I：

10.1007/s11042-020-08945-z

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Acquiring all-in-focus images is significant in the multi-media era. Limited by the depth-of-field of the optical lens, it is hard to acquire an image with all targets are clear. One possible solution is to merge the information of a few complementary images in the same scene. In this article, we employ a two-channel convolutional network to derive the clarity map of source images. Then, the clarity map is smoothed by using morphological filtering. Finally, the fusion image is constructed via merging the clear parts of source images. Experimental results prove that our approach has a better performance on both visual quality and quantitative evaluations than many previous fusion approaches.

引用

页码：34531 / 34543

页数：13