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Fused Image Quality Assessment Based on Human Vision

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Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1117))

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Abstract

Favorable human eye recognition is the only criterion for false color image fusion. In order to overcome this problem, a new method for detecting the fusion quality of pseudo-color images is proposed. This method makes full use of the structure-sensitive characteristics of human beings and approximates human vision through algorithm. The results show that this method can achieve better results.

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References

  1. Ji, H., Luo, X.: 3D scene reconstruction of landslide topography based on data fusion between laser point cloud and UAV image. Environ. Earth Sci. 78(17) (2019)

    Google Scholar 

  2. El-Hoseny, H.M., Kareh, Z.Z., Mohamed, W.A., Banby, G.M., Mahmoud, K.R., Faragallah, O.S., El-Rabaie, S., El-Madbouly, E., El-Samie, F.E.A.: An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching. Multimed. Tools Appl. 78(18), 26373–26397 (2019)

    Article  Google Scholar 

  3. Yao, Y.-C., Lin, H.-H., Chou, P.-H., Wang, S.-T., Chang, M.-C.: Differences in the interbody bone graft area and fusion rate between minimally invasive and traditional open transforaminal lumbar interbody fusion: a retrospective short-term image analysis. Eur. Spine J. 28(9), 2095–2102 (2019)

    Article  Google Scholar 

  4. Leger, T., Tacher, V., Majewski, M., Touma, J., Desgranges, P., Kobeiter, H.: Image fusion guidance for in situ laser fenestration of aortic stent graft for endovascular repair of complex aortic aneurysm: feasibility, efficacy and overall functional success. Cardiovasc. Interv. Radiol. 42(10), 1371–1379 (2019)

    Article  Google Scholar 

  5. Ch, M.M.I., Riaz, M.M., Iltaf, N., Ghafoor, A., Ahmad, A.: Weighted image fusion using cross bilateral filter and non-subsampled Contourlet transform. Multidimens. Syst. Signal Process. 30(4), 2199–2210 (2019)

    Article  Google Scholar 

  6. Kanmani, M., Narasimhan, V.: An optimal weighted averaging fusion strategy for remotely sensed images. Multidimens. Syst. Signal Process. 30(4), 1911–1935 (2019)

    Article  Google Scholar 

  7. Wu, S., Wu, W., Yang, X., Lu, L., Liu, K., Jeon, G.: Multifocus image fusion using random forest and hidden Markov model. Soft. Comput. 23(19), 9385–9396 (2019)

    Article  Google Scholar 

  8. Hou, R., Nie, R., Zhou, D., Cao, J., Liu, D.: Infrared and visible images fusion using visual saliency and optimized spiking cortical model in non-subsampled Shearlet transform domain. Multimed. Tools Appl. 78(20), 28609–28632 (2019)

    Article  Google Scholar 

  9. Sun, J., Wang, W., Zhang, K., Zhang, L., Lin, Y., Han, Q., Da, Q., Kou, L.: A multi-focus image fusion algorithm in 5G communications. Multimed. Tools Appl. 78(20), 28537–28556 (2019)

    Article  Google Scholar 

  10. Shuang, L., Deyun, C., Zhifeng, C., Ming, P.: Multi-feature fusion method for medical image retrieval using wavelet and bag-of-features. Comput. Assist. Surg. 24(1), 72–80 (2019)

    Article  Google Scholar 

  11. Dao, P.D., Mong, N.T., Chan, H.P.: Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene. GIScience Remote Sens. 56(8), 1148–1169 (2019)

    Article  Google Scholar 

  12. Yamada, R., Bassaco, B., Wise, C., Barnes, L., Golchin, N., Guimaraes, M.: Radiofrequency wire technique and image fusion in the creation of an endovascular bypass to treat chronic central venous occlusion. J. Vasc. Surg. Cases Innov. Tech. 5(3), 356–359 (2019)

    Article  Google Scholar 

  13. Jiang, M.-S., Yang, T., Xu, X.-L., Zhang, X.-D., Li, F.: Rapid microscope auto-focus method for uneven surfaces based on image fusion. Microsc. Res. Tech. 82(9), 1621–1627 (2019)

    Article  Google Scholar 

  14. Shopovska, I., Jovanov, L., Philips, W.: Deep visible and thermal image fusion for enhanced pedestrian visibility. Sensors 19(17), 3727 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Army Academy of Amored Forces, Changchun, China

    Lei Zhang, Zhao Yao & Ou Qi

  2. Logistic University of PAP, Tianjin, China

    Jian Han

Authors
  1. Lei Zhang
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  2. Zhao Yao
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  3. Ou Qi
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  4. Jian Han
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Corresponding author

Correspondence to Ou Qi .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

  2. University of Aizu, Fukushima, Japan

    Neil Yen

  3. Shanghai University, Shanghai, China

    Zheng Xu

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© 2020 Springer Nature Singapore Pte Ltd.

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Zhang, L., Yao, Z., Qi, O., Han, J. (2020). Fused Image Quality Assessment Based on Human Vision. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2019. Advances in Intelligent Systems and Computing, vol 1117. Springer, Singapore. https://doi.org/10.1007/978-981-15-2568-1_122

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