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Image Enhancement Using Adaptive Fractional Order Filter

Ayesha Heena1,*, Nagashettappa Biradar1, Najmuddin M. Maroof2, Surbhi Bhatia3, Arwa Mashat4, Shakila Basheer5

1 Department of Electronics and Communication Engineering, Bheemanna Khandre Institute of Technology, Bhalki, Visveswaraya Technological University, Belagavi, India
2 Department of Electronics and Communication Engineering, Khaja Banda Nawaz College of Engineering, Visveswaraya Technological University, Belagavi, India
3 Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Hofuf, Al-Ahsa, Saudi Arabia
4 Faculty of Computing & Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Science Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Ayesha Heena. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1409-1422. https://doi.org/10.32604/csse.2023.029611

Abstract

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images, Therefore, enhancement becomes the mandatory process before actual image processing should start. This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images, the proposed operators are based on Grunwald-Letnikov (G-L), Riemann-Liouville (R-L) and Caputo (Li & Xie), which are the definitions of fractional order calculus. In this fractional-order, differentiation is well focused on the enhancement of echocardiographic images. This provoked for developing a non-linear filter mask for image enhancement. The designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features, particularly in smooth areas. The novelty of the proposed method involves a procedure of partitioning the image into homogenous regions, details, and edges. Thereafter, a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the image. It is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative analysis. The wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement, histogram equalization, integer-order differential methods have been used. The proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous methods. The values obtained are very close (almost equal to 99.9%) to the original values of the average gradient and entropy of the images. The results of the simulation validate the effectiveness of the proposed algorithm.

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APA Style
Heena, A., Biradar, N., Maroof, N.M., Bhatia, S., Mashat, A. et al. (2023). Image enhancement using adaptive fractional order filter. Computer Systems Science and Engineering, 45(2), 1409-1422. https://doi.org/10.32604/csse.2023.029611
Vancouver Style
Heena A, Biradar N, Maroof NM, Bhatia S, Mashat A, Basheer S. Image enhancement using adaptive fractional order filter. Comput Syst Sci Eng. 2023;45(2):1409-1422 https://doi.org/10.32604/csse.2023.029611
IEEE Style
A. Heena, N. Biradar, N.M. Maroof, S. Bhatia, A. Mashat, and S. Basheer "Image Enhancement Using Adaptive Fractional Order Filter," Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1409-1422. 2023. https://doi.org/10.32604/csse.2023.029611



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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