A method to improve image quality by mixing images from two different image sources

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Authors

  • Le Khanh Thanh Institute of Military Technical Automation, Academy of Military Science and Technology
  • Le Ba Tuan Institute of Military Technical Automation, Academy of Military Science and Technology
  • Vu Quoc Huy (Corresponding Author) Institute of Military Technical Automation, Academy of Military Science and Technology

DOI:

https://doi.org/10.54939/1859-1043.j.mst.CAPITI.2024.161-167

Keywords:

Machine vision; Image processing; Improve image quality; Image matching.

Abstract

This article describes a method to improve the quality of infrared images by mixing images from two sources of conventional camera images and infrared images to observe the same scene. Two images from two different cameras do not have a consistent data structure: different resolutions, different fields of view (FOV), and different lens adjustments. To improve the quality of an image source, the images must first be converted to the same conditions. The two images are then mixed to enhance the quality of the infrared image. Experimental results show the effectiveness of the method when infrared image quality is improved.

References

[1]. Ying, Zhenqiang and Li, Ge and Ren, Yurui and Wang, Ronggang, and Wang, Wenmin, “A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework”, International Conference on Computer Analysis of Images and Patterns, Springer, pp.36-46, (2017). DOI: https://doi.org/10.1007/978-3-319-64698-5_4

[2]. Fu, Qingtao and Jung, Cheolkon and Xu, Kaiqiang, “Retinex-based perceptual contrast enhancement in images using luminance adaptation”, IEEE Access, volume 6, pp. 61277—61286, (2018). DOI: https://doi.org/10.1109/ACCESS.2018.2870638

[3]. Wang, Wencheng and Chen, Zhenxue and Yuan, Xiaohui and Wu, Xiaojin, “Adaptive image enhancement method for correcting low-illumination images”, Information Sciences, Elsevier, Volume 496, pp 25—41, (2019). DOI: https://doi.org/10.1016/j.ins.2019.05.015

[4]. Agrawal, Sanjay and Panda, Rutuparna and Mishro, PK and Abraham, Ajith, “ A novel joint histogram equalization based image contrast enhancement”, Journal of King Saud University-Computer and Information Sciences, Elsevier, (2019).

[5]. Hessel, Charles and Morel, Jean-Michel, “An extended exposure fusion and its application to single image contrast enhancement”, The IEEE Winter Conference on Applications of Computer Vision, pp. 137-146, (2020). DOI: https://doi.org/10.1109/WACV45572.2020.9093643

[6]. R. Brooks, S. Iyengar, “Multi-Sensor Fusion: Fundamentals and Applications”, Prentice Hall, (1998).

[7]. R. Luo and M. Kay, “Multisensor Integration and Fusion for Intelligent Machines and Systems”,

Ablex, Norwood, NJ, (1995).

[8]. H. Li and Y. Zhou, “Automatic visual/IR image registration,” Optical Engineering, 35(2), pp. 391- DOI: https://doi.org/10.1117/1.600908

, (1996).

[9]. R. Sharma and M.Pavel, “Multisensor image registration,” SID Digest, Society for Information

Display, XXVIII, pp. 951–954, (1997).

[10]. H. Li, B. Manjunath, and S. Mitra, “A contour-based approach to multisensor image registration,” IEEE Transactions on Image Processing, 4, No. 3, (1995). DOI: https://doi.org/10.1109/83.366480

[11]. D. G. Lowe, “Object Recognition from Local Scale-Invariant Features”, Proceedings of the Seventh 422 IEEE International Conference on Computer Vision, Vol. 2, pp. 1150–1157, (1999). DOI: https://doi.org/10.1109/ICCV.1999.790410

[12]. Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, “Speeded-Up Robust Features”, Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346–359, (2008). DOI: https://doi.org/10.1016/j.cviu.2007.09.014

[13]. Rublee E, Rabaud V, Konolige K, Bradski G, “ORB: An efficient alternative to SIFT or SURF”, 2011 International Conference on Computer Vision, pp. 2564–2571, (2011). DOI: https://doi.org/10.1109/ICCV.2011.6126544

[14]. Marius Muja, David G. Lowe, “FLANN: Fast Library for Approximate Nearest Neighbors”, ACM International Conference on Multimedia (ACM MM), pp. 951-954, (2009).

[15]. Martin A. Fischler và Robert C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM (ACM), pp.120-133, (1981). DOI: https://doi.org/10.1145/358669.358692

[16]. Jiayi Ma, Yong Ma, Chang Li, “Infrared and visible image fusion methods and applications: A survey”, Information Fusion Volume 45, pp. 153-178, (2019). DOI: https://doi.org/10.1016/j.inffus.2018.02.004

Published

01-04-2024

How to Cite

Lê Khánh Thành, Lê Bá Tuấn, and Vũ Quốc Huy. “A Method to Improve Image Quality by Mixing Images from Two Different Image Sources”. Journal of Military Science and Technology, no. CAPITI, Apr. 2024, pp. 161-7, doi:10.54939/1859-1043.j.mst.CAPITI.2024.161-167.

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