Joint patch clustering-based adaptive dictionary and sparse representation for multi-modality image fusion
For the image fusion method using sparse representation, the adaptive dictionary and fusion rule have a great influence on the multi-modality image fusion, and the maximum L 1 norm fusion rule may cause gray inconsistency in the fusion result. In order to solve this problem, we proposed an improved...
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| Published in: | Machine vision and applications Vol. 33; no. 5 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.09.2022
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| ISSN: | 0932-8092, 1432-1769 |
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| Abstract | For the image fusion method using sparse representation, the adaptive dictionary and fusion rule have a great influence on the multi-modality image fusion, and the maximum
L
1
norm fusion rule may cause gray inconsistency in the fusion result. In order to solve this problem, we proposed an improved multi-modality image fusion method by combining the joint patch clustering-based adaptive dictionary and sparse representation in this study. First, we used a Gaussian filter to separate the high- and low-frequency information. Second, we adopted the local energy-weighted strategy to complete the low-frequency fusion. Third, we used the joint patch clustering algorithm to reconstruct an over-complete adaptive learning dictionary, designed a hybrid fusion rule depending on the similarity of multi-norm of sparse representation coefficients, and completed the high-frequency fusion. Last, we obtained the fusion result by transforming the frequency domain into the spatial domain. We adopted the fusion metrics to evaluate the fusion results quantitatively and proved the superiority of the proposed method by comparing the state-of-the-art image fusion methods. The results showed that this method has the highest fusion metrics in average gradient, general image quality, and edge preservation. The results also showed that this method has the best performance in subjective vision. We demonstrated that this method has strong robustness by analyzing the parameter’s influence on the fusion result and consuming time. We extended this method to the infrared and visible image fusion and multi-focus image fusion perfectly. In summary, this method has the advantages of good robustness and wide application. |
|---|---|
| AbstractList | For the image fusion method using sparse representation, the adaptive dictionary and fusion rule have a great influence on the multi-modality image fusion, and the maximum
L
1
norm fusion rule may cause gray inconsistency in the fusion result. In order to solve this problem, we proposed an improved multi-modality image fusion method by combining the joint patch clustering-based adaptive dictionary and sparse representation in this study. First, we used a Gaussian filter to separate the high- and low-frequency information. Second, we adopted the local energy-weighted strategy to complete the low-frequency fusion. Third, we used the joint patch clustering algorithm to reconstruct an over-complete adaptive learning dictionary, designed a hybrid fusion rule depending on the similarity of multi-norm of sparse representation coefficients, and completed the high-frequency fusion. Last, we obtained the fusion result by transforming the frequency domain into the spatial domain. We adopted the fusion metrics to evaluate the fusion results quantitatively and proved the superiority of the proposed method by comparing the state-of-the-art image fusion methods. The results showed that this method has the highest fusion metrics in average gradient, general image quality, and edge preservation. The results also showed that this method has the best performance in subjective vision. We demonstrated that this method has strong robustness by analyzing the parameter’s influence on the fusion result and consuming time. We extended this method to the infrared and visible image fusion and multi-focus image fusion perfectly. In summary, this method has the advantages of good robustness and wide application. For the image fusion method using sparse representation, the adaptive dictionary and fusion rule have a great influence on the multi-modality image fusion, and the maximum L1 norm fusion rule may cause gray inconsistency in the fusion result. In order to solve this problem, we proposed an improved multi-modality image fusion method by combining the joint patch clustering-based adaptive dictionary and sparse representation in this study. First, we used a Gaussian filter to separate the high- and low-frequency information. Second, we adopted the local energy-weighted strategy to complete the low-frequency fusion. Third, we used the joint patch clustering algorithm to reconstruct an over-complete adaptive learning dictionary, designed a hybrid fusion rule depending on the similarity of multi-norm of sparse representation coefficients, and completed the high-frequency fusion. Last, we obtained the fusion result by transforming the frequency domain into the spatial domain. We adopted the fusion metrics to evaluate the fusion results quantitatively and proved the superiority of the proposed method by comparing the state-of-the-art image fusion methods. The results showed that this method has the highest fusion metrics in average gradient, general image quality, and edge preservation. The results also showed that this method has the best performance in subjective vision. We demonstrated that this method has strong robustness by analyzing the parameter’s influence on the fusion result and consuming time. We extended this method to the infrared and visible image fusion and multi-focus image fusion perfectly. In summary, this method has the advantages of good robustness and wide application. |
| ArticleNumber | 69 |
| Author | Wu, Yang Zhao, Jun Qiang Yu, Yi Wang, Chang |
| Author_xml | – sequence: 1 givenname: Chang surname: Wang fullname: Wang, Chang organization: The Third Affiliated Hospital of Xinxiang Medical University, School of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis – sequence: 2 givenname: Yang surname: Wu fullname: Wu, Yang organization: School of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis – sequence: 3 givenname: Yi surname: Yu fullname: Yu, Yi organization: School of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis – sequence: 4 givenname: Jun Qiang surname: Zhao fullname: Zhao, Jun Qiang email: 280905788@qq.com organization: School of Medical Engineering, Xinxiang Medical University |
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| Cites_doi | 10.1007/s11831-021-09540-7 10.1109/TIM.2009.2026612 10.1109/ICIP.2013.6738268 10.1016/j.inffus.2015.03.003 10.1166/jmihi.2019.2669 10.1016/j.inffus.2007.04.003 10.1016/j.inffus.2016.12.009 10.1109/JSTSP.2011.2112332 10.1016/j.inffus.2013.12.002 10.1364/AO.55.001814 10.1016/S1874-1029(08)60174-3 10.1016/j.inffus.2006.04.001 10.1016/j.infrared.2014.04.003 10.1117/12.235981 10.1016/j.inffus.2010.03.002 10.1016/j.neucom.2014.07.003 10.1016/j.inffus.2018.02.004(2019) 10.1016/j.asoc.2016.03.028 10.1016/j.inffus.2014.10.004 10.1109/TSP.2006.881199 10.1109/ICME.2017.8019435 |
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| Keywords | Adaptive learning dictionary Sparse representation Multi-modality Fusion metric Image fusion |
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Fusion, https://doi.org/10.1016/j.inffus.2018.02.004(2019). YuNQiuTBiFWangAImage features extraction and fusion based on joint sparse representationIEEE J. Select. Topics Signal Process.2011551074108210.1109/JSTSP.2011.2112332 Kaur H, Koundal D, Kadyan V.: Image fusion techniques: a survey. Arch. Comput. Methods Eng., https://doi.org/10.1007/s11831-021-09540-7 (2021). LiYLiFYBaiBDImage fusion via nonlocal sparse K-SVD dictionary learningAppl. Opt.2016551814182310.1364/AO.55.001814 KimMHanDKKoHJoint patch clustering-based dictionary learning for multimodal image fusionInf. Fusion20162719821410.1016/j.inffus.2015.03.003 Piella, G., Heijmans, H.: A new quality metric for image fusion, Proceedings of 10th International Conference on Image Processing, Barcelona, Spain, 173–176 (2003). WangQNieRCZhouDMJinXHeKJYuJFImage fusion algorithm using PCNN model parameters of multi-objective particle swarm optimizationJ. Image Graph.201621112951306 Shen, Y., Xiong, H., Dai, W.: Multiscale dictionary learning for hierarchical sparse representation, IEEE International Conference on Multimedia & Expo. IEEE (2017). Li, T.: Research on multi sensor image information fusion method and application, Central South University (2001). AharonMEladMBrucksteinAK-SVD: an algorithm for designing over complete dictionaries for sparse representationIEEE Trans. Signal Process.200654114311432210.1109/TSP.2006.881199 WangZMaYMedical image fusion using m-PCNNInf. Fusion200892176185393756610.1016/j.inffus.2007.04.003 BroussardRPRogersSKPhysiologically motivated image fusion using pulse-coupled neural networksProc. Appl. Sci. Artif. Neural Netw.1996II37238410.1117/12.235981 WangCZhaoZZRenQQMulti-modality anatomical and functional medical image fusion based on simplified-spatial frequency-pulse coupled neural networks and region energy-weighted average strategy in non-sub sampled contourlet transform domainJ. Med. Imag. Health Inf.201991017102710.1166/jmihi.2019.2669 EckhornRReitbockHJArndtMDickePA neural network for feature linking via synchronous activity: results from cat visual cortex and from simulationsCan. J. Microbiol.1989468759763 YinHTSparse representation with learned multiscale dictionary for image fusionNeurocomputing201514860061010.1016/j.neucom.2014.07.003 Alexander, T.: TNO Image Fusion Dataset. figshare. Dataset. 2014, 10.6084/m9.figshare.1008029.v1 LiSYangBHuJPerformance comparison of different multi-resolution transforms for image fusionInf. Fusion2011122748410.1016/j.inffus.2010.03.002 NejatiMSamaviSShiraniSMulti-focus Image Fusion Using Dictionary-Based Sparse Representation [J]Information Fusion201525728410.1016/j.inffus.2014.10.004 ZhangJYLiangJLImage fusion based on pulse-coupled neural networks [J]Computer Simulation2004211102104 QuXBImage fusion algorithm based on spatial frequency motivated pulse coupled neural networks in NSCT domainACTA AUTOM ATICA SINICA2008341508151410.1016/S1874-1029(08)60174-3 KongWWZhangLJLeiYNovel fusion method for visible light and infrared images based on NSST–SF–PCNNInfr. Phys. Technol.201465110311210.1016/j.infrared.2014.04.003 GoshtasbyANikolovSImage fusion: advances in the state of the artInf. Fusion20078211411810.1016/j.inffus.2006.04.001 NejatiMSamaviSKarimiNSurface area-based focus criterion for multi-focus image fusionInf. Fusion2017361528429510.1016/j.inffus.2016.12.009 XinLGWangRLWangGYRemote Sensing Image Fusion based on DCTAppl. Res. Comput.2005022004242243 AP James (1322_CR4) 2014; 19 M Kim (1322_CR25) 2013; 2013 1322_CR18 B Yang (1322_CR19) 2010; 59 HT Yin (1322_CR20) 2015; 148 JY Zhang (1322_CR10) 2004; 21 M Nejati (1322_CR6) 2017; 36 C Wang (1322_CR14) 2019; 9 M Nejati (1322_CR30) 2015; 25 RP Broussard (1322_CR8) 1996; II Z Wang (1322_CR13) 2008; 9 S Li (1322_CR5) 2011; 12 N Yu (1322_CR17) 2011; 5 M Aharon (1322_CR22) 2006; 54 1322_CR27 1322_CR26 Y Li (1322_CR23) 2016; 55 1322_CR29 1322_CR28 M Kim (1322_CR24) 2016; 27 X Xu (1322_CR15) 2016; 48 A Goshtasby (1322_CR2) 2007; 8 1322_CR3 LG Xin (1322_CR21) 2005; 022 1322_CR1 Q Wang (1322_CR16) 2016; 21 R Eckhorn (1322_CR7) 1989; 46 1322_CR9 XB Qu (1322_CR11) 2008; 34 WW Kong (1322_CR12) 2014; 65 |
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J., AlexBecker, J.: The Whole Brain Atlas on CD-ROM, 1999, Amsterdam, Holland, Lippincott Williams & Wilkins. – reference: KimMHanDKKoHMultimodal image fusion via sparse representation with local patch dictionariesIEEE Int. Conf. Image Process.201320131301130510.1109/ICIP.2013.6738268 – reference: Shen, Y., Xiong, H., Dai, W.: Multiscale dictionary learning for hierarchical sparse representation, IEEE International Conference on Multimedia & Expo. IEEE (2017). – reference: YinHTSparse representation with learned multiscale dictionary for image fusionNeurocomputing201514860061010.1016/j.neucom.2014.07.003 – reference: ZhangJYLiangJLImage fusion based on pulse-coupled neural networks [J]Computer Simulation2004211102104 – reference: Kaur H, Koundal D, Kadyan V.: Image fusion techniques: a survey. Arch. Comput. 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Neural Netw.1996II37238410.1117/12.235981 – reference: Piella, G., Heijmans, H.: A new quality metric for image fusion, Proceedings of 10th International Conference on Image Processing, Barcelona, Spain, 173–176 (2003). – reference: WangCZhaoZZRenQQMulti-modality anatomical and functional medical image fusion based on simplified-spatial frequency-pulse coupled neural networks and region energy-weighted average strategy in non-sub sampled contourlet transform domainJ. Med. Imag. Health Inf.201991017102710.1166/jmihi.2019.2669 – reference: YuNQiuTBiFWangAImage features extraction and fusion based on joint sparse representationIEEE J. Select. Topics Signal Process.2011551074108210.1109/JSTSP.2011.2112332 – reference: NejatiMSamaviSKarimiNSurface area-based focus criterion for multi-focus image fusionInf. 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| SubjectTerms | Adaptive learning Algorithms Clustering Communications Engineering Computer Science Computer vision Dictionaries Image processing Image Processing and Computer Vision Image quality Infrared imagery Machine learning Networks Original Paper Pattern Recognition Representations Robustness Vision systems |
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| Title | Joint patch clustering-based adaptive dictionary and sparse representation for multi-modality image fusion |
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