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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:
Convolution;Neural networks;Optical resolving power;All-in-focus image;Computation efficiency;Convolutional neural network;High-resolution fused images;Multifocus image fusion;Reconstruction networks;Super resolution;Superresolution methods;Image fusion
期刊:
International Journal of Wavelets, Multiresolution and Information Processing
ISSN:
0219-6913
年:
2017
卷:
15
期:
4
页码:
1750037
基金类别:
This paper is supported by the National Natural Science Foundation of China (Nos. 61102108), Scientific Research Fund of Hunan Provincial Education Department (Nos. 16B225, YB2013B039), the Natural Science Foundation of Hunan Province (Nos. 2016JJ3106), Young talents program of the University of South China, the construct programof key disciplines in USC (No. NHXK04), and Scientific Research Fund of Hengyang Science and Technology Bureau (Nos. 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 a...

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