Volume 13 Issue 4
Aug.  2020
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CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217
Citation: CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217

Method of enhancing the quality of in-line holographic images for micro-milling tool

doi: 10.37188/CO.2019-0217
Funds:  (The National Key Research and Development Plan Project (No. 2018YFB1107403); The “111” Project of China (No. D17017); Jilin Province Scientific and Technological Development Program (No. 20190101005JH and No. 20180201057GX).)
More Information
  • Corresponding author: yuhuadong@cust.edu.cn
  • Received Date: 2019-11-13
  • Rev Recd Date: 2019-12-09
  • Available Online: 2020-06-29
  • Publish Date: 2020-08-01
  • When tool setting with digital in-line holography, the zero-order image and defocused twin-image can form strong and complex background noise, which gets superimposed on the real image and seriously reduces the quality of the reconstructed image. To improve quality of interferential images in digital in-line holography, a holographic image enhancement method using an improved self-snake model is proposed. The improved self-snake model selects a diffusion intensity according to the gradient of the initial image. The experimental results show that the improved self-snake model can avoid the appearance of jagged edges and “pseudo-contours” caused by large gradient background noise during the diffusion process. This improvement outweighs the shortcomings of the self-snake model in holographic imaging. In addition, compared with the phase retrieval and multi-plane reproduction approaches, the improved self-snake model filtering method proposed in this paper not only has better suppression for interferential images but also can enhance the edge of the tool, which is conducive to the actualization of tool-setting using on digital in-line holography.
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Method of enhancing the quality of in-line holographic images for micro-milling tool

doi: 10.37188/CO.2019-0217
Funds:  (The National Key Research and Development Plan Project (No. 2018YFB1107403); The “111” Project of China (No. D17017); Jilin Province Scientific and Technological Development Program (No. 20190101005JH and No. 20180201057GX).)

Abstract: When tool setting with digital in-line holography, the zero-order image and defocused twin-image can form strong and complex background noise, which gets superimposed on the real image and seriously reduces the quality of the reconstructed image. To improve quality of interferential images in digital in-line holography, a holographic image enhancement method using an improved self-snake model is proposed. The improved self-snake model selects a diffusion intensity according to the gradient of the initial image. The experimental results show that the improved self-snake model can avoid the appearance of jagged edges and “pseudo-contours” caused by large gradient background noise during the diffusion process. This improvement outweighs the shortcomings of the self-snake model in holographic imaging. In addition, compared with the phase retrieval and multi-plane reproduction approaches, the improved self-snake model filtering method proposed in this paper not only has better suppression for interferential images but also can enhance the edge of the tool, which is conducive to the actualization of tool-setting using on digital in-line holography.

CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217
Citation: CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217

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