Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder
Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. M...
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2018-06-01
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Series: | Journal of Algorithms & Computational Technology |
Online Access: | https://doi.org/10.1177/1748301817741001 |
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doaj-825d7b3b76554f269536a51d36acc0932020-11-25T04:01:10ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262018-06-011210.1177/1748301817741001Image fusion based on shift invariant shearlet transform and stacked sparse autoencoderPeng-Fei WangXiao-Qing LuoXin-Yi LiZhan-Cheng ZhangStacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.https://doi.org/10.1177/1748301817741001 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peng-Fei Wang Xiao-Qing Luo Xin-Yi Li Zhan-Cheng Zhang |
spellingShingle |
Peng-Fei Wang Xiao-Qing Luo Xin-Yi Li Zhan-Cheng Zhang Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder Journal of Algorithms & Computational Technology |
author_facet |
Peng-Fei Wang Xiao-Qing Luo Xin-Yi Li Zhan-Cheng Zhang |
author_sort |
Peng-Fei Wang |
title |
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
title_short |
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
title_full |
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
title_fullStr |
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
title_full_unstemmed |
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
title_sort |
image fusion based on shift invariant shearlet transform and stacked sparse autoencoder |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3018 1748-3026 |
publishDate |
2018-06-01 |
description |
Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations. |
url |
https://doi.org/10.1177/1748301817741001 |
work_keys_str_mv |
AT pengfeiwang imagefusionbasedonshiftinvariantshearlettransformandstackedsparseautoencoder AT xiaoqingluo imagefusionbasedonshiftinvariantshearlettransformandstackedsparseautoencoder AT xinyili imagefusionbasedonshiftinvariantshearlettransformandstackedsparseautoencoder AT zhanchengzhang imagefusionbasedonshiftinvariantshearlettransformandstackedsparseautoencoder |
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1724447533818707968 |