Abstract
In the system of the image defect detection system, during image acquisition and transmission, the salt-and-pepper Noise will adversely affect the subsequent processing and recognition. To eliminate the salt-and-pepper noise effectively, a defect image denoising algorithm based on ant colony optimization wavelet threshold is improved in this paper. Firstly, the basic principle of wavelet denoising is analyzed theoretically, and a compromise threshold function and a GCV optimal threshold selection method are adopted. It uses ant colony algorithm to optimize the wavelet threshold, which greatly improves the speed and accuracy of the optimal threshold. Using standard soft threshold method, GCV threshold optimization method and the ant colony optimization wavelet threshold method, the defect image of the lens is denoised. The results of experiment indicate that the algorithm can remove the salt-and-pepper noise in the image of defective lenses more effectively than the other two algorithms, and improve the accuracy of the lens detection. This algorithm is also suitable for general image denoising.
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References
Ai, Z., Shi, G.: Wavelet transform used in image denoising. Sci. Technol. Rev. 01, 102–106 (2010)
Li, Z.-S., Li, W.-L., Yao, J.-G., & Yang, Y.-J.: On-side detection of pollution level of insulators based on infrared-thermal-image processing. Proc. CSEE 30(4), 132–138 (2010)
Donoho, D.L.: De-nosing by soft-thresholding. IEEE Trans. Inform. Theory 41, 613–627 (1995)
Ma, G.-B., Xiao, P.-R.: Study on wavelet-based image denoising. Ind. Control Comput. 05, 91–92 (2013)
Lin, J., Sun, S.-X., Wen, W.: Wavelet threshold denoising based on particle swarmal optimization algorithm. Comput. Eng. Appl. 04, 204–207 (2007)
Zhang, L., Fang, Z.-J., Wang, S.-Q., et al.: Multiwavelet adaptive denoising method based on genetic algorithm. J. Infrared Millim. Waves 28(01), 77–80 (2009)
Zhang, F.-J., Zhou, Y., Cao, J.-G.: Fast implementation of MALLAT algorithm and its application. Autom. Instrum. (06), 4–5+27 (2004)
Mallat, S.G., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Inf. Theory 38, 617–642 (1992)
Zheng, D., Zhou, Y., Jing, N.: Generalized cross validation for wavelet denoising based on GCV rule. Chin. J. Sci. Instrum. S3, 2268–2270 (2006)
He, X.-H.: Research on optimized ant colony algorithm of image edge detection. Comput. Technol. Dev. 02, 60–63 (2017)
Han, Y., Shi, P.: Image segmentation based on improved ant colony algorithm. Comput. Eng. Appl. 18, 5–7 (2004)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. Bradford Books, Bradford (2004)
Zong, Z.: The application and improvement of ant colony algorithm. Appl. Comput. Technol. 01, 115 (2017)
Yao, H., Ma, G.: Flaws detection system for resin lenses based on machine vision. Laser Optoelectron. Process 11, 112–119 (2013)
Gao, Y., Diao, Y., Mao, J.: Research on image denoising based on wavelet optimization threshold. J. Tonghua Teach. Coll. 04, 25–27 (2011)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xue, P., Niu, X., Zhu, X., Wang, H., Chen, J. (2018). Wavelet Threshold Denoising of ACO Optical Lens Image. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_34
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DOI: https://doi.org/10.1007/978-3-319-73317-3_34
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