research-article

Multi-focus Color Image Fusion Scheme Using NSST and Focus Region Detection

Authors:

Dong LiuAuthors Info & Claims

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

Pages 7 - 11

https://doi.org/10.1145/3195588.3195606

Published: 16 March 2018 Publication History

Abstract

Aimed at low contrast, color distortion, loss of edge details and discontinuous phenomenon at border of focused region problems in the existing color image fusion methods, this paper proposes a novel color image fusion scheme by using nonsubsampled shearlet transform (NSST) and k nearest neighbors (KNN) matting which preserve the fine edge of the decision map fully. Firstly, the primitive fusion image is obtained by NSST. Secondly, the structural similarity maps are drawn according to the structural similarity relationship between the source images and the primitive fusion image, and then utilizing the mathematical morphology operation to detect the focus and defocus region and the unknown region, which can build an accurate trimap, so the final fine decision map is obtained by using KNN matting. Finally reconstruct the image based on the color combination equation. Experimental results indicate that the proposed scheme performs well and has superiorities over other existing typical and state of art ones in both subjective visual performance and objective criteria to a certain extent.

References

[1]

Zhang, Xiuqiong, Q. Chen, and T. Men. 2009. "Comparison of fusion methods for the infrared and color visible images." IEEE International Conference on Computer Science and Information Technology IEEE, 421--424.

[2]

Jin, X., Jiang, Q., Yao, S., Zhou, D., Nie, R., & Hai, J., et al. 2017. A survey of infrared and visual image fusion methods. Infrared Physics & Technology, 85.

[3]

Naidu, V., & Raol, J. R. 2008. Pixel-level image fusion using wavelets and principal component analysis. Defence Science Journal, 58(3), 338--352.

[4]

Qi, L., Feng, H., Xu, Z., Bian, M., & Su, S. 2002. Digital image sharpness evaluation function. Acta Photonica Sinica, 31(6), 736--738.

[5]

Li, Shutao, Kang, Xudong, Hu, & Jianwen, et al. 2013. Image matting for fusion of multi-focus images in dynamic scenes. Information Fusion,14(2), 147--162.

Digital Library

[6]

Li, H., Manjunath, B. S., & Mitra, S. K. 1994. Multi-Sensor Image Fusion using the Wavelet Transform. IEEE International Conference on Image Processing (Vol.1, pp. 235--245).

[7]

Cunha, A. L. D., Zhou, J., & Do, M. N. 2005. Nonsubsampled contourilet transform: filter design and applications in denoising. IEEE International Conference on Image Processing (Vol.1, pp. I-749--52). IEEE.

[8]

Jin, X., Zhou, D., Yao, S., Nie, R., Yu, C., & Ding, T. 2016. Remote sensing image fusion method in cielab color space using nonsubsampled shearlet transform and pulse coupled neural networks. Journal of Applied Remote Sensing, 10(2), 025023.

[9]

Huang, Z., Ding, M., & Zhang, X. 2017. Medical image fusion based on non-subsampled shearlet transform and spiking cortical model. Journal of Medical Imaging & Health Informatics, 7(1), 229--234.

[10]

Kong, W. W. 2013. Multi-sensor image fusion based on nsst domain i 2 cm. Electronics Letters, 49(13), 802--803.

[11]

Chen, Q., Li, D., & Tang, C. K. 2013. Knn matting. IEEE Transactions on Pattern Analysis & Machine Intelligence, 35(9), 2175--2188.

Digital Library

[12]

Li, C., & Bovik, A. C. 2010. Content-partitioned structural similarity index for image quality assessment. Signal Processing Image Communication, 25(7), 517--526.

Digital Library

[13]

Levin, A., Lischinski, D., & Weiss, Y. 2007. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis & Machine Intelligence, 30(2), 228--242.

Digital Library

[14]

Zambotti-Villela, L., Yamasaki, S. C., Villarroel, J. S., Alponti, R. F., & Silveira, P. F. 2014. Novel fusion method for visible light and infrared images based on nsst-sf-pcnn. Infrared Physics & Technology, 65(7), 103--112.

[15]

Li sun et al. 2015. Multi-focus image fusion based on regional segment and guided filter. Transaction of Beijing Institute of Technology, 35(6), 634--638.

[16]

Huang, W., & Jing, Z. 2007. Evaluation of focus measures in multi-focus image fusion. Pattern Recognition Letters, 28(4), 493--500.

Digital Library

Cited By

Bhat SKoundal D(2021)Multi-focus image fusion techniques: a surveyArtificial Intelligence Review10.1007/s10462-021-09961-754:8(5735-5787)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10462-021-09961-7
Luo HHou RQi W(2019)A Novel Infrared and Visible Image Fusion Using Low-rank Representation and Simplified Dual Channel Pulse Coupled Neural NetworkProceedings of the 2019 International Conference on Artificial Intelligence and Computer Science10.1145/3349341.3349472(583-589)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3349341.3349472
Liu YZhou DNie RHou RDing ZXie R(2018)Brain CT and MRI medical image fusion scheme Using NSST And Dictionary Learning2018 IEEE 4th International Conference on Computer and Communications (ICCC)10.1109/CompComm.2018.8780625(1579-1583)Online publication date: Dec-2018
https://doi.org/10.1109/CompComm.2018.8780625

Index Terms

Multi-focus Color Image Fusion Scheme Using NSST and Focus Region Detection
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Other conferences

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

March 2018

125 pages

ISBN:9781450364683

DOI:10.1145/3195588

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Wuhan Univ.: Wuhan University, China
University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2018

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ICMIP 2018

ICMIP 2018: 2018 the 3rd International Conference on Multimedia and Image Processing

March 16 - 18, 2018

Guiyang, China

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Cited By

Bhat SKoundal D(2021)Multi-focus image fusion techniques: a surveyArtificial Intelligence Review10.1007/s10462-021-09961-754:8(5735-5787)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10462-021-09961-7
Luo HHou RQi W(2019)A Novel Infrared and Visible Image Fusion Using Low-rank Representation and Simplified Dual Channel Pulse Coupled Neural NetworkProceedings of the 2019 International Conference on Artificial Intelligence and Computer Science10.1145/3349341.3349472(583-589)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3349341.3349472
Liu YZhou DNie RHou RDing ZXie R(2018)Brain CT and MRI medical image fusion scheme Using NSST And Dictionary Learning2018 IEEE 4th International Conference on Computer and Communications (ICCC)10.1109/CompComm.2018.8780625(1579-1583)Online publication date: Dec-2018
https://doi.org/10.1109/CompComm.2018.8780625

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