Enhancement Infrared-Visible Image Fusion Using the Integration of Stationary Wavelet Transform and Fuzzy Histogram Equalization

Rusul Basheer Khazal; Nada Jasim Habeeb

doi:10.51173/jt.v4i4.700

Authors

Rusul Basheer Khazal Technical College of management - Baghdad, Middle Technical University, Baghdad, Iraq.
Nada Jasim Habeeb Technical College of management - Baghdad, Middle Technical University, Baghdad, Iraq.

DOI:

https://doi.org/10.51173/jt.v4i4.700

Keywords:

Image Fusion, Wavelet Transform, Multimodal, DWT, SWT, Visible and Infrared, Fuzzy Histogram Equalization

Abstract

Image fusion is the process of merging two or more images to obtain complementary features from source images. Imaging techniques in real-world applications provide images with a different texture than the other, where visible images provide spatial information while infrared images provide spectral information. Hence the importance of image fusion, which aims to combine spatial and spectral information in one image. Wavelet transform is a method used in the process of image fusion as feature extraction, and images are decomposed into a series of low and high-frequency subbands. Wavelet transform provides images with good representation and is a multi-resolution analysis. However, the resulting image after the wavelet-based fusion process has low-quality information which is blurry. In addition, infrared images by their nature suffer from blur. In this paper, a novel image fusion method has been proposed to enhance visible-infrared image fusion using the integration of stationary wavelet transform and fuzzy histogram equalization. Firstly, input the images. Secondly, preprocessing the images. Thirdly, stationary wavelet transform has been used for decomposing the images in 2 levels. Fourthly, Averaging fusion rule is used for fusing the approximation coefficients. Finally, fuzzy histogram equalization is used in reconstructing the level 2 process to obtain the final enhanced image. The performance of the proposed method is evaluated by using seven metrics that proved the superiority of the proposed method compared to the standard methods.

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References

Chaowei Duan, Changda Xing, Shanshan Lu, Zhisheng Wang, “Two‐scale fusion method of infrared and visible images via parallel saliency features,” IET Image Processing, Vol. 14, Issue 16, p. 4412 – 4423, Dec 2020, dx.doi.org/10.1049/iet-ipr.2020.1165.

Deepak Gambhir, “Image Fusion using PCA Based Fusion Rule in Wavelet Domain,” International Journal of Trend in Scientific Research and Development, Vol. 4, no. 6, pp. 462-465, Oct 2020, www.ijtsrd.com/papers/ijtsrd33367.pdf.

G. Xiao, D. Bavirisetti, G. Liu, X. Zhang, “Image Fusion,” Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press 2020, doi.org/10.1007/978-981-15-4867-3.

Nahed Tawfik, Heba Elnemr, Mahmoud Fakhr, Moawad Dessouky, Fathi El-Samie, “Survey study of multimodality medical image fusion methods,” Springer Multimedia Tools and Applications, Vol. 80, No. 4, pp. 6369-6396, 2020, doi.org/10.1007/s11042-020-08834-5.

Yi Deng, Chanfei Li, Zili Zhang, Dan Wang, “Image Fusion Method for Infrared and Visible Light Images based on SWT and Regional Gradient,” 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference, pp. 976-979, Dec 2017, doi.org/10.1109/ITOEC.2017.8122499.

N. Aishwarya, C. Thangammal, “Visible and Infrared image fusion using DTCWT and adaptive combined clustered dictionary,” Infrared Physics & Technology, vol. 93 no. 17, pp. 300-309, Sep 2018, doi.org/10.1016/ j.infrared.2018.08.013.

Nada Habeeb, “Performance Enhancement of Medical Image Fusion Based on DWT and Sharpening Wiener Filter,” Jordanian Journal of Computers and Information Technology, Vol. 07, No. 02, pp. 118-125, June 2021, dx.doi.org/10.5455/jjcit.71-1610049522.

Yuzhen Lu, “Out-of-focus Blur: Image De-blurring, “Michigan State University, East Lansing, MI 48824, 524 S. Shaw Lane, 105A A.W. Farrall Hall (May 6, 2016). (PDF) Out-of-focus Blur: Image De-blurring (researchgate.net).

Adnan Abdulazeez, Diyar Zeebaree, Dildar Abdulqader, “Wavelet Applications in Medical Images: A Review,” Engineering & Management, Vol. 83, pp. 17265 – 17276, June 2020.

Lingchao Zhan, Yi Zhuang, Longda Huang, “Infrared and visible images fusion method based on discrete wavelet transform,” Journal of Computers, Vol. 28, no. 2, pp. 57-71, Apr 2017, dx.doi.org/10.3966/199115592017042802005.

Nada Habeeb, “Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion,” Iraqi Journal of Science, Vol. 62, No. 9, pp: 3228-3230, Sep 2021, doi: 10.24996/ijs.2021.62.9.35.

Javad Aghamaleki, Alireza Ghorbani, “Infrared and visible image fusion based on optimal segmenting and contour extraction,” SN Applied Sciences, 2021, 10.1007/s42452-020-04050-w.

Kapil Joshia, Madhu Kirolab, Sumit Chaudharyc, Manoj Diwakard, N. Joshi, “Multi-focus image fusion using Discrete Wavelet Transform method,” International Conference on Advances in Engineering Science Management & Technology, May 2019, dx.doi.org/10.2139/ssrn.3383141.

Sarwar Khan, Muzammil Khan, Yasser Alharbi, “Multi Focus Image Fusion using Image Enhancement Techniques with Wavelet Transformation,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 5, Jan 2020, dx.doi.org/10.14569/IJACSA.2020.0110555.

Chengtao Cai, Xin Ding, “Fusion of Infrared and Visible Image Based on HIS and Wavelet Transform,” IEEE Chinese Control and Decision Conference, June 2018, doi.org/10.1109/CCDC43067.2018.

Manel Rhif, Ali Ben Abbes, Imed Farah, Beatriz Martínez, Yanfang Sang, “Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review,” Applied Sciences, Vol. 9, no. 7, Mar 2017, DOI: 10.3390/app9071345.

Huma Qayyum, Muhammad Majid, Syed Muhammad Anwar, Bilal Khan, “Facial Expression Recognition Using Stationary Wavelet Transform Features” Hindawi, Mathematical Problems in Engineering, 2017, doi.org/10.1155/2017/9854050.

Manoj Diwakar, Amrendra Tripathi, Kapil Joshi, Ayush Sharma, Prabhishek Singh, Minakshi Memoria, Neeraj kumar, “A comparative review: Medical image fusion using SWT and DWT,” Materials Today: Proceedings, Vol. 37, pp. 3411-3416, Aug 2020, doi.org/10.1016/j.matpr.2020.09.278.

Prashant Dwivedy, Anjali Potnis, Shahbaz Soofi, Madhuram Mishra, “Comparative Study of MSVD, PCA, DCT, DTCWT, SWT and Laplacian Pyramid Based Image Fusion,” Proceeding International conference on Recent Innovations is Signal Processing and Embedded Systems, India, 2017, pp. 269-273, doi.org/10.1109/RISE.2017.8378165.

U. Sesadri, C. Nagaraju, M. Ramakrishna,” Fuzzy Histogram Equalization for Image Enhancement.” International Journal of Emerging Technology and Advanced Engineering, Vol. 8, Issue 7, July 2018, Dr.Ramakrishna-M-Fuzzy-Histogram-Equalization-for-Image-Enhancement.pdf (vemanait.edu.in).

Fatima Abbas, Nabeel Mirza, Amel Abbas, Layla Abbas, “Enhancement of Wheat Leaf Images Using Fuzzy-Logic Based Histogram Equalization to Recognize Diseases,” Iraqi Journal of Science, Vol. 61, No. 9, pp. 2408-2417, 2020, DOI: 10.24996/ijs.2020.61.9.27.

Khursheed Dar, Sumit Mittal, “A Dynamic Fuzzy Histogram Equalization for High Dynamic Range Images by Using Multi-Scale Retinex Algorithm,” International Conference on Intelligent Communication and Computational Research, India, 2020.

Said Pertuz, Domenec Puig, Miguel Garcia, “Analysis of focus measure operators for shape-from-focus,” Pattern Recognition, Vol. 46, pp. 1415-1432, May 2013, https://doi.org/10.1016/j.patcog.2012.11.011.