Authors : Thatipally Sharanya; M. Sampath

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/xs8bkrm8

Scribd : https://tinyurl.com/3wj23dfd

DOI : https://doi.org/10.38124/ijisrt/25dec1562

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the field of image processing, edge detection plays a crucial role in extracting meaningful structural information from visual data. It is widely used in applications such as object recognition, feature extraction, and motion analysis in computer vision. Among various edge detection techniques, the Canny edge detector is recognized for its optimal performance in detecting true edges while minimizing false detections. This work presents a comparative study of edge detection methods with a special focus on the Canny algorithm, highlighting its efficiency and precision over conventional techniques. To enhance its performance on blurred and noisy images, a Median filter is employed as a preprocessing step. The Median filter effectively reduces noise while preserving edges, resulting in a more accurate and robust edge detection pipeline. The proposed improvement demonstrates superior edge preservation in challenging visual conditions, validating the effectiveness of integrating edge-preserving noise reduction with traditional Canny edge detection.

Keywords : Canny Edge Detector, Median Filtering, FPGA-Based Designs, Image Denoising.

References :

1. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698.
2. Gota, A., & Min, Z. J. (2013). Analysis and Comparison on Image Restoration Algorithms Using MATLAB. International Journal of Engineering Research & Technology (IJERT) Vol, 2, 1350-1360.
3. Mahalakshmi, A.,& Shanthini, B. (2016, January). A survey on image deblurring. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.
4. Flusser, J., Farokhi, S., Hoschl, C., Suk, T., Zitov ¨ a, B., ´ & Pedone, M. (2015). Recognition of images degraded by Gaussian blur. IEEE transactions on Image Processing, 25(2), 790-806.
5. Ramya, S.,& Christial, T. M. (2011, March). Restoration of blurred images using Blind Deconvolution Algorithm. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 496-499). IEEE.
6. Sada, M. M., & Mahesh, M. G. (2018). Image deblurring techniques—a detail review. Int. J. Sci. Res. Sci. Eng. Technol, 4(2), 15.
7. Verma, R., & Ali, J. (2013). A comparative study of various types of image noise and efficient noise removal techniques. International Journal of advanced research in computer science and software engineering, 3(10).
8. Syahrian, N. M., Risma, P., & Dewi, T. (2017). Vision-based pipe monitoring robot for crack detection using canny edge detection method as an image processing technique. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 243- 250.
9. Sekehravani, E. A., Babulak, E., & Masoodi, M. (2020). Implementing canny edge detection algorithm for noisy image. Bulletin of Electrical Engineering and Informatics, 9(4), 1404-1410.
10. Yadav, S., Jain, C., &Chugh, A. (2016). Evaluation of image deblurring techniques. International Journal of Computer Applications, 139(12), 32- 36.