Research Article
Wavelet Threshold Denoising of ACO Optical Lens Image
@INPROCEEDINGS{10.1007/978-3-319-73317-3_34, author={Ping Xue and Xiangyong Niu and Xiaohui Zhu and Hongmin Wang and Jihua Chen}, title={Wavelet Threshold Denoising of ACO Optical Lens Image}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Lens defect image Wavelet denoising Ant colony optimization algorithm Salt-and-pepper noise}, doi={10.1007/978-3-319-73317-3_34} }- Ping Xue
Xiangyong Niu
Xiaohui Zhu
Hongmin Wang
Jihua Chen
Year: 2018
Wavelet Threshold Denoising of ACO Optical Lens Image
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_34
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.