Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment
Abstract
:1. Introduction
2. Principle of Noise-Tolerant Edge Detection Algorithm
2.1. Theoretical Basis
- (1)
- is the derivative of with respect to , if it is equal to zero at , then the wavelet transform has local extreme value in .
- (2)
- If for any point in the neighborhood of point satisfies , and in the left neighborhood or the right neighborhood strictly meet the above inequality relations, is called the maximum point of wavelet transform modulus in the scale of ; is called the maximum modulus of wavelet transform modulus on the point of .
- (3)
- On the plane of , if there is a curve that every point is a modulus maximum point of , it is called a modulus maximum curve.
- (1)
- (2)
- gets the local modulus maximum at point .
2.2. Algorithm Basis
- (1)
- Singular point of signal usually corresponds to the maximum value of the wavelet transform coefficient.
- (2)
- There is a correlation relationship between wavelet coefficients in different scales, that is, when the parent coefficient has a larger value, its four sub-coefficients also have a larger value.
- (3)
- Noise and edge signal have a different Lipschitz exponent property, which means that wavelet transform modulus of noise decreases with the increase of scale, and wavelet transform modulus of image edge signal increases with the increase of scale.
- (1)
- First, select a kind of wavelet filter. The application effect of wavelet transform is closely dependent on the selection of wavelet function. Wavelet selection should comprehensively consider various performances according to the application requirements. Generally speaking, at a certain scale, the number of the detected extreme points has a linear relationship with the number of wavelet disappearing moments. The aim of this paper is to do edge detection, and a filter with small vanishing moments can be selected to get more high-frequency information in transformation domain. See Section 3 of this paper for detailed discussion.
- (2)
- Second, wavelet transform is performed at a certain scale. For digital images, the number of wavelet coefficients decreases by ration of . If the original image is small, the coefficients after several layers of wavelet transformation will become too less to support the detection and analysis algorithm. In this paper, for image with resolution of , three-layer wavelet transform is adopted for coefficients analysis.
- (3)
- The correlation relationship between wavelet transform coefficients is used for edge detection and denoising. This is composed of two steps: the first, from bottom to top, preserve the important coefficients by using the idea of activation function of neural network unit; the second, from top to bottom, eliminate unimportant coefficients by using outlier point search judgment algorithm. See Section 2.3 of this article for detailed algorithm.
- (4)
- Wavelet inverse transform is applied to the coefficients preserved after the above treatment.
- (5)
- According to the actual processing effect, some mathematical morphology algorithm can be adopted to remove the isolated noise points, so as to obtain a clearer and complete edge image.
2.3. Process of Denoising by Interlayer Correlation of Wavelet Coefficients
2.3.1. Process of Wavelet Coefficients Adopting Idea of Neural Network Activation Function
2.3.2. Judgment for Isolated Coefficients
3. Design of Rational Coefficients Biorthogonal Wavelet Filters
3.1. Construction of Biorthogonal Complete Reconstruction Wavelet Filters
3.2. Construction of Even Length Symmetric Compactly-Supported Biorthogonal Wavelet Filters
3.3. Length 8-4 Rational Coefficients Symmetric Compactly-Supported Biorthogonal Wavelet Filters
4. Experiments
4.1. Edge Detection for Images under Noisy Environments
4.2. Edge Detection for Noisy Image by Different Wavelet Filters
4.3. Edge Detection for Image with Gray-Gradient Edge under Noisy Environment
4.4. Edge Detection for High-Speed Moving Target Image under Noisy Environment
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Left-Top | Right-Top | Right-Down | Left-Down | Center | MSE | |
---|---|---|---|---|---|---|
Original | (98,88) | (98,168) | (158,168) | (158,88) | (128,128) | 0 |
Gaussian Figure 13f | (95,87) | (95,170) | (162,170) | (162,87) | (128.5,128.5) | 0.0460 |
Salt and pepper Figure 15f | (96,88) | (96,169) | (161,169) | (161,88) | (128.5,128.5) | 0.0211 |
Speckle Figure 15f | (96,87) | (96,170) | (162,170 | (162,87) | (129,128.5) | 0.0424 |
Left-Top | Right-Top | Right-Down | Left-Down | Enclosing Rectangle Center | Fitting Circle Center | Fitting Circle Radius | MSE | |
---|---|---|---|---|---|---|---|---|
Original | (78,78) | (78,178) | (178,178) | (178,78) | (128,128) | (128,128) | 40 | 0 |
Figure 23a | (72,72) | (72,177) | (177,177) | (177,72) | (124.5,124.5) | (125.0,125.0) | 43.88 | 0.2004 |
Figure 23b | (71,71) | (71,181) | (181,181) | (181,71) | (126,126) | (126.2,126.2) | 43.58 | 0.2441 |
Figure 23c | (73,73) | (73,184) | (184,184) | (184,73) | (128.5,128.5) | (129.5,129.5) | 44.48 | 0.1639 |
Figure 23d | (73,73) | (73,177) | (177,177) | (177,73) | (125,125) | (125.6,125.6) | 43.64 | 0.1333 |
Figure 23e | (72,72) | (72,185) | (185,185) | (185,72) | (128.5,128.5) | (127.2,127.2) | 44.32 | 0.2637 |
Figure 23f | (79,79) | (79,177) | (177,177) | (177,79) | (128,128) | (128.1,128.1) | 42.20 | 0.0243 |
Figure 24f | (76,75) | (76,177) | (177,177) | (177,75) | (126.5,126) | (127.5,127.5) | 42.97 | 0.0458 |
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Han, F.; Liu, B.; Zhu, J.; Zhang, B. Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment. Sensors 2019, 19, 343. https://doi.org/10.3390/s19020343
Han F, Liu B, Zhu J, Zhang B. Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment. Sensors. 2019; 19(2):343. https://doi.org/10.3390/s19020343
Chicago/Turabian StyleHan, Fangfang, Bin Liu, Junchao Zhu, and Baofeng Zhang. 2019. "Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment" Sensors 19, no. 2: 343. https://doi.org/10.3390/s19020343
APA StyleHan, F., Liu, B., Zhu, J., & Zhang, B. (2019). Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment. Sensors, 19(2), 343. https://doi.org/10.3390/s19020343