Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Different types of noise interference lead to low accuracy of image edge detection and severe loss of feature extraction details. A new noise-robust edge detection method is proposed, which uses a set of multiscale anisotropic morphological directional derivatives to extract the edge map of an input...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.19162-19173
Main Authors: Yu, Xiaohang, Wang, Xinyu, Liu, Jie, Xie, Rongrong, Li, Yunhong
Format: Article
Language:English
Subjects:
anisotropic morphological directional derivatives
Detectors
Edge detection
Feature extraction
Figure of merit
Gray-scale
ground truth
Image edge detection
Interference
Matched filters
Morphology
multiscale
Noise
Noise measurement
Noise robustness
Random noise
Robustness
Spatial resolution
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Summary:Different types of noise interference lead to low accuracy of image edge detection and severe loss of feature extraction details. A new noise-robust edge detection method is proposed, which uses a set of multiscale anisotropic morphological directional derivatives to extract the edge map of an input image. The main advantage of the method is that high edge resolution is maintained while reducing noise interference. The following five parts form the whole framework of this paper. First, multiscale anisotropic morphologic directional derivatives (MSAMDDs) are proposed to filter and obtain the local gray value of the image. Second, the edge strength map (ESM) is extracted by using spatial matching filters. In the third stage, multiscale edge direction maps (EDMs) based on the directional matched filters are fused, and the new EDM is constructed. Fourth, edge contours are obtained by embedding the ESM and the EDM into the standard route of Canny detection. Finally, the precision-recall curve and Pratt's figure of merit (FOM) are used to evaluate the proposed method against eight state-of-the-art methods on three data sets. The experimental results show that the proposed method can perform better for noise-free (F-measure value of 0.776) and Gaussian noise (FOM value of 95.75%) and attains the best performance in impulse noise images (highest FOM value of 98.90%).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3149520