Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework

Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shear...
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Autor*in:	Jiang Qian [verfasserIn] Liu Yadong [verfasserIn] Dai Jindun [verfasserIn] Fu Xiaofei [verfasserIn] Jiang Xiuchen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 83484-83494
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:83484-83494

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2924033

Katalog-ID:	DOAJ084943432

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spelling	10.1109/ACCESS.2019.2924033 doi (DE-627)DOAJ084943432 (DE-599)DOAJ909afd9280e9441cad41b48614a8cca1 DE-627 ger DE-627 rakwb eng TK1-9971 Jiang Qian verfasserin aut Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance. Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN Electrical engineering. Electronics. Nuclear engineering Liu Yadong verfasserin aut Dai Jindun verfasserin aut Fu Xiaofei verfasserin aut Jiang Xiuchen verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 83484-83494 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:83484-83494 https://doi.org/10.1109/ACCESS.2019.2924033 kostenfrei https://doaj.org/article/909afd9280e9441cad41b48614a8cca1 kostenfrei https://ieeexplore.ieee.org/document/8742596/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 83484-83494
allfields_unstemmed	10.1109/ACCESS.2019.2924033 doi (DE-627)DOAJ084943432 (DE-599)DOAJ909afd9280e9441cad41b48614a8cca1 DE-627 ger DE-627 rakwb eng TK1-9971 Jiang Qian verfasserin aut Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance. Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN Electrical engineering. Electronics. Nuclear engineering Liu Yadong verfasserin aut Dai Jindun verfasserin aut Fu Xiaofei verfasserin aut Jiang Xiuchen verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 83484-83494 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:83484-83494 https://doi.org/10.1109/ACCESS.2019.2924033 kostenfrei https://doaj.org/article/909afd9280e9441cad41b48614a8cca1 kostenfrei https://ieeexplore.ieee.org/document/8742596/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 83484-83494
allfieldsGer	10.1109/ACCESS.2019.2924033 doi (DE-627)DOAJ084943432 (DE-599)DOAJ909afd9280e9441cad41b48614a8cca1 DE-627 ger DE-627 rakwb eng TK1-9971 Jiang Qian verfasserin aut Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance. Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN Electrical engineering. Electronics. Nuclear engineering Liu Yadong verfasserin aut Dai Jindun verfasserin aut Fu Xiaofei verfasserin aut Jiang Xiuchen verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 83484-83494 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:83484-83494 https://doi.org/10.1109/ACCESS.2019.2924033 kostenfrei https://doaj.org/article/909afd9280e9441cad41b48614a8cca1 kostenfrei https://ieeexplore.ieee.org/document/8742596/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 83484-83494
allfieldsSound	10.1109/ACCESS.2019.2924033 doi (DE-627)DOAJ084943432 (DE-599)DOAJ909afd9280e9441cad41b48614a8cca1 DE-627 ger DE-627 rakwb eng TK1-9971 Jiang Qian verfasserin aut Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance. Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN Electrical engineering. Electronics. Nuclear engineering Liu Yadong verfasserin aut Dai Jindun verfasserin aut Fu Xiaofei verfasserin aut Jiang Xiuchen verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 83484-83494 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:83484-83494 https://doi.org/10.1109/ACCESS.2019.2924033 kostenfrei https://doaj.org/article/909afd9280e9441cad41b48614a8cca1 kostenfrei https://ieeexplore.ieee.org/document/8742596/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 83484-83494
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source	In IEEE Access 7(2019), Seite 83484-83494 volume:7 year:2019 pages:83484-83494
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callnumber-first	T - Technology
author	Jiang Qian
spellingShingle	Jiang Qian misc TK1-9971 misc Image fusion misc multi-focus and multi-sensor misc multi-scale decomposition misc saliency map misc PCNN misc Electrical engineering. Electronics. Nuclear engineering Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework
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topic_title	TK1-9971 Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework Image fusion multi-focus and multi-sensor multi-scale decomposition saliency map PCNN
topic	misc TK1-9971 misc Image fusion misc multi-focus and multi-sensor misc multi-scale decomposition misc saliency map misc PCNN misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Image fusion misc multi-focus and multi-sensor misc multi-scale decomposition misc saliency map misc PCNN misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Image fusion misc multi-focus and multi-sensor misc multi-scale decomposition misc saliency map misc PCNN misc Electrical engineering. Electronics. Nuclear engineering
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title	Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework
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title_full	Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework
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author_browse	Jiang Qian Liu Yadong Dai Jindun Fu Xiaofei Jiang Xiuchen
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title_sort	image fusion method based on structure-based saliency map and fdst-pcnn framework
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title_auth	Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework
abstract	Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance.
abstractGer	Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance.
abstract_unstemmed	Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance.
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title_short	Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework
url	https://doi.org/10.1109/ACCESS.2019.2924033 https://doaj.org/article/909afd9280e9441cad41b48614a8cca1 https://ieeexplore.ieee.org/document/8742596/ https://doaj.org/toc/2169-3536
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author2	Liu Yadong Dai Jindun Fu Xiaofei Jiang Xiuchen
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doi_str	10.1109/ACCESS.2019.2924033
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up_date	2024-07-04T01:12:22.185Z
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