Improvement of Image Fusion Using Multi-Resolution Color Images
Main Article Content
Abstract
The image fusion technique was employed to generate a high-resolution image from a low-resolution one. A high scale was achieved by increasing the resolution of an image through interpolation, which relies on pixel neighbours. Multispectral images (low-resolution images with high resolution) from different satellites were utilized in this work for obtaining a high-resolution image. The crucial part of image fusion is image registration, which depends on obtaining a good high-resolution image. A scale-invariant feature transform (SIFT) was utilized to receive control points with an affine transform. Correct control points are determined depending on the scale of the image (downscaling followed by upscaling), which is necessary for stabilizing match control points between images. Control points were identified using the SIFT algorithm, which utilizes distance and angle to determine the correct match points. A different scale image is used to detect and correspond to the correct control points between the two images, thereby speeding up the process. Also, image scaling with contrast stretching was utilized for preprocessing to stretch brightness to obtain high-quality image features. A morphological operation is applying post-processing to images (scale, contrast stretching, and radiometric operation). Final image fusion is obtained using the colour model (luminosity, hue, and saturation). A metric criterion, root mean square error (RMSE), with peak signal-to-noise ratio (PSNR) is utilizing for determining the goodness of this process, which is used in image fusion.
Article Details
Issue
Section

This work is licensed under a Creative Commons Attribution 4.0 International License.
© 2023 The Author(s). Published by College of Science, University of Baghdad. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License.
How to Cite
References
1. H. Lestiana and Sukristiyanti, IOP Conf. Ser. Earth Environ. Sci. 118, 012047 (2018). https://doi.org/10.1088/1755-1315/118/1/012047.
2. L. Sharma, S. Sengupta, and B. Kumar, J. Phys.: Conf. Ser. 1714, 012051 (2021). https://doi.org/10.1088/1742-6596/1714/1/012051.
3. I. M. Hayder, H. A. Younis, and H. Abdul-Kareem Younis, J. Phys.: Conf. Ser. 1279, 012072 (2019). https://doi.org/10.1088/1742-6596/1279/1/012072.
4. S. Ashraf, L. Brabyn, and B. J. Hicks, Appl. Geog. 32, 619 (2012). https://doi.org/10.1016/j.apgeog.2011.07.010.
5. H.-S. Jung and S.-W. Park, Sensors 14, 24425 (2014). https://doi.org/10.3390/s141224425.
6. A. A. Hamed, A. Al-Safar, and N. A. Taha, Ibn AL-Haitham J. Pure Appl. Sci. 30, 236 (2017). https://doi.org/10.30526/30.3.1624.
7. Y. Li, C. Huang, J. Hou, J. Gu, G. Zhu, and X. Li, Agricult. Fores. Met. 244-245, 82 (2017). https://doi.org/10.1016/j.agrformet.2017.05.023.
8. A. A. N. Al-Jasim, T. A. Naji, and A. H. Shaban, Iraqi J. Sci. 63, 4131 (2022). https://doi.org/10.24996/ijs.2022.63.9.40.
9. R. A. Abdulwahab, L. A. Al-Ani, and A. H. Shaban, AIP Conf. Proc. 3018, 020009 (2023). https://doi.org/10.1063/5.0171974.
10. Y. Dufournaud, C. Schmid, and R. Horaud, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Hilton Head, SC, USA IEEE, 2000). p. 612.
11. F. F. Sabins Jr and J. M. Ellis, Remote Sensing: Principles, Interpretation, and Applications (USA, Waveland Press, Inc, 2020).
12. M. I. Abd-Almajied, L. E. George, and R. S. Hameed, Iraqi J. Sci., 2635 (2023). https://doi.org/10.24996/ijs.2023.64.5.44.
13. H. Lin, P. Du, W. Zhao, L. Zhang, and H. Sun, 2010 3rd International Congress on Image and Signal Processing (Yantai, China IEEE, 2010). p. 2184.
14. J. Zhu and M. Ren, Comput. Math. Meth. Med. 2014, 926312 (2014). https://doi.org/10.1155/2014/926312.
15. M. Shaharom and K. Tahar, Int. J. Geoinfo. 19, 13 (2023). https://doi.org/10.52939/ijg.v19i1.2495.
16. S. Avidan and A. Shamir, ACM Transact. Graph. 26, 1 (2023). https://doi.org/10.1145/1239451.1239461.
17. I. Ashraf, S. Hur, and Y. Park, IEEE Access 5, 8250 (2017). https://doi.org/10.1109/ACCESS.2017.2699686.
18. W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction (London, Springer Nature, 2022).
19. Y. Liu, M. He, Y. Wang, Y. Sun, and X. Gao, IEEE Access 10, 95411 (2022). https://doi.org/10.1109/ACCESS.2022.3204657.
20. W. F. Ahmed and M. I. Abd-Almajied, J. Phys.: Conf. Ser. 2857, 012038 (2024). https://doi.org/10.1088/1742-6596/2857/1/012038.
21. M. I. Abd-Almajied, L. E. George, and K. M. Abood, J. Theo. Appl. Infi. Tech. 97, 740 (2019).
22. S. Salamon, Modern Differential Geometry of Curves and Surfaces with Mathematica (New York, Chapman and Hall/CRC, 2017).
23. A. A. Goshtasby, Image Registration: Principles, Tools and Methods (London, UK, Springer Science & Business Media, 2012).
24. E. A. Al-Hilo and R. Zehwar, Int. J. Adv. Comput. Sci. Applicat. (IJACSA) 6, 112. https://doi.org/10.14569/IJACSA.2015.060518.
25. T. O. Hodson, Geosci. Model Dev. 15, 5481 (2022). https://doi.org/10.5194/gmd-15-5481-2022.
26. R. S. Hameed and L. E. George, AIP Conf. Proc. 2769, 020046 (2023). https://doi.org/10.1063/5.0129346.
27. J. T. L. Thong, K. S. Sim, and J. C. H. Phang, Scanning 23, 328 (2001). https://doi.org/10.1002/sca.4950230506.
28. J. M. S. Ismail, Ibn AL-Haitham J. Pure Appl. Sci. 30, 237 (2017). https://doi.org/10.30526/30.1.1073.