Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Deepak Raj, D.M.a | Arulmurugan, A.b; * | Shankar, G.c | Arthi, A.d | Panthagani, Vijaya Babue | Sandeep, C.H.f
Affiliations: [a] Department of Computer Science and Engineering Alliance College of Engineering and Design, Anekal, Bangalore, India | [b] Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, Tamilnadu, India | [c] Department of Computer Science and Engineering R.M.D. Engineering College, Chennai, India | [d] Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, India | [e] Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhrapradesh, India | [f] School of Computer Science and Artificial Intelligence, SR University, Warangal, India
Correspondence: [*] Corresponding author. A. Arulmurugan, Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai-603203, Tamilnadu, India. E-mail: [email protected].
Abstract: The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes.
Keywords: Edge detection, image pre-processing, image smoothing, low resolution image, metrics
DOI: 10.3233/JIFS-235332
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]