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Defect detection of optical elements surfaces using curvelet transform
Author(s): Linfu Li; JianJun Chen; Jinbao Huang
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Paper Abstract

The complex components often require a variety of processes in the manufacturing process, such as turning, milling, grinding, polishing etc. Therefore, it is inevitable to produce defect features on the surface of the component. The defective surfaces will directly affect the performance of the entire component, so it must be identified during production and inspection. In this paper, based on the excellent curve feature recognition and sparse representation of curvelet transform, a defect extraction method based on the curvelet transform for feature separation in transform domain is proposed. The effectiveness of the method is proved by simulation results and experimental examples.

Paper Details

Date Published: 18 November 2019
PDF: 8 pages
Proc. SPIE 11189, Optical Metrology and Inspection for Industrial Applications VI, 1118919 (18 November 2019); doi: 10.1117/12.2536996
Show Author Affiliations
Linfu Li, Guizhou Minzu Univ. (China)
JianJun Chen, Xinjiang Medical Univ. (China)
Jinbao Huang, Guizhou Minzu Univ. (China)


Published in SPIE Proceedings Vol. 11189:
Optical Metrology and Inspection for Industrial Applications VI
Sen Han; Toru Yoshizawa; Song Zhang; Benyong Chen, Editor(s)

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