Open AccessEnhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation
Author(s) -
Rakesh Ranjan,
Vinay Avasthi
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19362
Subject(s) - sharpening , artificial intelligence , edge detection , computer vision , fuzzy logic , computer science , canny edge detector , image processing , pixel , pattern recognition (psychology) , digital image , enhanced data rates for gsm evolution , edge enhancement , mathematics , image (mathematics)
In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.