Jun. 2011

Article Contents

Chinese Optics> 2011> 4(3): 283-292.

LV Heng-yi, LIU Yang, XUE Xu-cheng. Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation[J]. Chinese Optics, 2011, 4(3): 283-292.

Citation:

LV Heng-yi, LIU Yang, XUE Xu-cheng. Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation[J].Chinese Optics, 2011, 4(3): 283-292.

LV Heng-yi, LIU Yang, XUE Xu-cheng. Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation[J]. Chinese Optics, 2011, 4(3): 283-292.

Citation:

LV Heng-yi, LIU Yang, XUE Xu-cheng. Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation[J].Chinese Optics, 2011, 4(3): 283-292.

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Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation

1.
Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;
2.
Department of Control Science and Engineering,Harbin Institute of Technology,Harbin 150001,China

Received Date:21 Feb 2011
Rev Recd Date:23 Apr 2011
Publish Date:25 Jun 2011

Abstract

Abstract

In order to enhance the auto-focusing capability of a imaging system, a difference image extraction method is proposed. Meanwhile, a evaluation function based on wavelets and a region selecting technique using Particle Swarm Optimization(PSO) are given. The principle and superiority of the difference image extraction method are introduced. Then a selecting criterion and a searching method using PSO for region evaluation are described, and the parameters for wavelet based evaluation function are compared and optimized. Finally, emulation experiments are practiced to analyze the advantages and disadvantages of traditional and proposed methods. Results indicate that the proposed method can offer a higher resolution to the focusing curve due to the difference image extraction, new focusing window and focus evaluation. Moreover, it has improved the focusing speed by 170 ms and provided the focusing precision about 2.3 m. Furthermore, its focusing effects are always satisfactory with different initial positions.
- auto-focusing,
- difference edge extraction,
- focus region selection,
- Particle Swarm Optimization(PSO),
- defination evaluation function,
- wavelet transform

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References (1)

References

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