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Multi-focus image fusion and super-resolution with convolutional neural network

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成果类型:
期刊论文
作者:
Yang, Bin*;Zhong, Jinying;Li, Yuehua;Chen, Zhongze
通讯作者:
Yang, Bin
作者机构:
[Chen, Zhongze; Li, Yuehua; Yang, Bin; Zhong, Jinying] Univ South China, Coll Elect Engn, Hengyang 421001, Peoples R China.
通讯机构:
[Yang, Bin] U
Univ South China, Coll Elect Engn, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Multi-focus image fusion;super-resolution;convolutional neural networks
期刊:
International Journal of Wavelets, Multiresolution and Information Processing
ISSN:
0219-6913
年:
2017
卷:
15
期:
4
页码:
1750037:1-1750037:15
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61102108]; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [16B225, YB2013B039]; Natural Science Foundation of Hunan ProvinceNatural Science Foundation of Hunan Province [2016JJ3106]; Young talents program of the University of South China; construct programof key disciplines in USC [NHXK04]; Scientific Research Fund of Hengyang Science and Technology Bureau [2015KG51]
机构署名:
本校为第一且通讯机构
院系归属:
电气工程学院
摘要:
The aim of multi-focus image fusion is to create a synthetic all-in-focus image from several images each of which is obtained with different focus settings. However, if the resolution of source images is low, the fused images with traditional fusion method would be also in low-quality, which hinders further image analysis even the fused image is all-in-focus. This paper presents a novel joint multi-focus image fusion and super-resolution method via convolutional neural network (CNN). The first level network features of different source images are fused with the guidance of the local clarity ca...

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