Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review

Hewa Majeed  Zangana; Ayaz Khalid  Mohammed; Firas  Mahmood Mustafa

doi:10.25139/ijair.v6i1.8217

Hewa Majeed Zangana IT Department, Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
Ayaz Khalid Mohammed Computer System Department, Ararat Technical Private Institute, Kurdistan Region, Iraq
Firas Mahmood Mustafa Chemical Engineering Department, Technical College of Engineering, Duhok Polytechnic, Duhok, Iraq

https://doi.org/10.25139/ijair.v6i1.8217

Abstract views: 48 ,

PDF downloads: 33

Keywords: Deep Learning, Edge Detection, Image Enhancement, Image Processing, Study Literatu, Literature Review Study

Abstract

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.

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References

A. H. Abdel-Gawad, L. A. Said, and A. G. Radwan, "Optimised edge detection technique for brain tumor detection in MR images," IEEE Access, vol. 8, pp. 136243–136259, 2020.

J. Mehena, "Medical image edge detection using modified morphological edge detection approach," International Journal of Computer Sciences and Engineering, vol. 7, no. 6, pp. 523–528, 2019.

K. Park, M. Chae, and J. H. Cho, "Image pre-processing method of machine learning for edge detection with image signal processor enhancement," Micromachines (Basel), vol. 12, no. 1, p. 73, 2021.

A. Jawdekar and M. Dixit, "Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework.," Traitement Du Signal, vol. 40, no. 1, 2023.

E. A. Sekehravani, E. Babulak, and M. Masoodi, "Implementing canny edge detection algorithm for noisy image," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1404–1410, 2020.

A. Kushwah, K. Gupta, A. Agrawal, G. Jain, and G. Agrawal, "A Review: Comparative Study of Edge Detection Techniques.," International Journal of Advanced Research in Computer Science, vol. 8, no. 5, 2017.

A. Kumar and S. Raheja, "Edge detection using guided image filtering and enhanced ant colony optimisation," Procedia Comput Sci, vol. 173, pp. 8–17, 2020.

M. Sudhakara and M. J. Meena, "An edge detection mechanism using L* A* B color-based contrast enhancement for underwater images," Indonesian J. of Elec. Engin. and Com. Sci, vol. 18, pp. 41–48, 2020.

M. A. Ansari, D. Kurchaniya, and M. Dixit, "A comprehensive analysis of image edge detection techniques," International Journal of Multimedia and Ubiquitous Engineering, vol. 12, no. 11, pp. 1–12, 2017.

A. Banharnsakun, "Artificial bee colony algorithm for enhancing image edge detection," Evolving Systems, vol. 10, no. 4, pp. 679–687, 2019.

S.-M. Hou, C.-L. Jia, Y.-B. Wanga, and M. Brown, "A review of the edge detection technology," Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC), vol. 1, no. 2, pp. 26–37, 2021.

J. Jing, S. Liu, G. Wang, W. Zhang, and C. Sun, "Recent advances on image edge detection: A comprehensive review," Neurocomputing, vol. 503, pp. 259–271, 2022.

K. Kaur, N. Jindal, and K. Singh, "Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and its application in image enhancement," Signal Processing, vol. 180, p. 107852, 2021.

M. Mittal et al., "An efficient edge detection approach to provide better edge connectivity for image analysis," IEEE access, vol. 7, pp. 33240–33255, 2019.

Z. A. Mustafa, B. A. Abrahim, A. Omara, A. A. Mohammed, I. A. Hassan, and E. A. Mustafa, "Reduction of speckle noise and image enhancement in ultrasound image using filtering technique and edge detection," J Clin Eng, vol. 45, no. 1, pp. 51–65, 2020.

R. Ranjan and V. Avasthi, "Enhanced edge detection technique in digital images using optimised fuzzy operation," Webology, vol. 19, no. 1, pp. 5402–5416, 2022.

S. Saxena, Y. Singh, B. Agarwal, and R. C. Poonia, "Comparative analysis between different edge detection techniques on mammogram images using PSNR and MSE," Journal of Information and optimisation Sciences, vol. 43, no. 2, pp. 347–356, 2022.

S. C. Shekar and D. J. Ravi, "Image enhancement and compression using edge detection technique," International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 5, 2017.

R. Sun et al., "Survey of image edge detection," Frontiers in Signal Processing, vol. 2, p. 826967, 2022.

M. Versaci and F. C. Morabito, "Image edge detection: A new approach based on fuzzy entropy and fuzzy divergence," International Journal of Fuzzy Systems, vol. 23, no. 4, pp. 918–936, 2021.