Removal of magnification chromatism in optoelectronic full color holography

WANG Tao; YU Ying-jie; ZHENG Hua-dong

doi:10.3788/OPE.20111906.1414

您当前的位置：

首页 >

文章列表页 >

Removal of magnification chromatism in optoelectronic full color holography

Article | 更新时间：2020-08-12

- Removal of magnification chromatism in optoelectronic full color holography
- Optics and Precision Engineering Vol. 19, Issue 6, Pages: 1414-1420(2011)
- 作者机构：
  
  上海大学精密机械工程系上海,200072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111906.1414
  CLC： O438.1
- Received：06 July 2010，
  
  Revised：27 October 2010，
  
  Published Online：25 June 2011，
  
  Published：25 June 2011
- 稿件说明：
移动端阅览
王涛, 于瀛洁, 郑华东. 彩色全息光电再现倍率色差的消除[J]. 光学精密工程, 2011,19(6): 1414-1420

WANG Tao, YU Ying-jie, ZHENG Hua-dong. Removal of magnification chromatism in optoelectronic full color holography[J]. Editorial Office of Optics and Precision Engineering, 2011,19(6): 1414-1420
王涛, 于瀛洁, 郑华东. 彩色全息光电再现倍率色差的消除[J]. 光学精密工程, 2011,19(6): 1414-1420 DOI： 10.3788/OPE.20111906.1414.

WANG Tao, YU Ying-jie, ZHENG Hua-dong. Removal of magnification chromatism in optoelectronic full color holography[J]. Editorial Office of Optics and Precision Engineering, 2011,19(6): 1414-1420 DOI： 10.3788/OPE.20111906.1414.

摘要

针对基于空间光调制器的彩色全息光电再现系统在利用计算全息图进行光电再现时会由于三色激光波长不同造成再现像出现倍率色差

影响再现效果的问题

本文重点分析了倍率色差产生的原因。为了消除倍率色差

提高彩色光电再现像的质量

提出了在全息图计算过程中对物信息进行差别采样来消除倍率色差。基于时分复用方法搭建了彩色全息光电再现系统

以迭代傅里叶变换型全息图为例

对处理后的计算全息图进行了光电再现。实验表明

提出的方法可以消除波长不同造成的色差(33.6%(红色)

12.5%(绿色))

实验结果验证了本文所提出方法的可行性。

Abstract

As the magnification chrometism caused from different wavelengths of color lasers in the optoelectric reconstruction based on a spatial light modulator effects on the quality of reconstructed images

this paper focuses on the reason that the magnification chromatism arises.In order to remove the magnification chromatism

it proposes a method to sample the three color objects with disparity in the process of hologram computing to improve the image quality. A full color electro-holographic system based on a time shearing method is established and holograms computed by the Iterative Fourier Transform Algorithm (IFTA) are reconstructed. The experiment indicates that the magnification chromatism 33.6% for red and 12.5% for blue can be both removed

which shows that the method proposed is verified by experiment results.

关键词

Keywords

references

MASTORCOSTAS P A,THEOCHARIS J B. A recurrent fuzzy-neural model for dynamic system identification[J]. IEEE Trans. Syst., Man and Cyber. Part B: Cybernetics.,2002,32(2):176-190.[2] JUANG C F. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithm[J]. IEEE Trans. Fuzzy Systems, 2002,10(2):155-170.[3] 郑晓虎,朱荻. 模糊神经网络在UV-LIGA工艺优化中的应用[J]. 光学精密工程,2006,14(1):139-144. ZHENG X H,ZHU D. Application of fuzzy neural network to optimizing UV-LIGA process[J].Opt. Precision Eng., 2006,14(1):139-144. (in Chinese)[4] 张玲瑄 ,贾振元. 微细电火花加工放电状态逐级映射检测[J]. 光学精密工程,2010,18(3):663-669. ZHANG L X, JIA ZH Y. Successive mapping detection of micro EDM discharge state[J]. Opt. Precision Eng., 2010,18(3):663-669. (in Chinese)[5] 李勇,王珂,张立保,等. 多断层融合的肺CT肿瘤靶区超分辨率重建[J]. 光学精密工程,2010,18(5):1213-1218. LI Y,WANG K,ZHANG L B,et al..Super-resolution reconstruction of pulmonary nodules based on CT multi-section fusion[J]. Opt. Precision Eng., 2010,18(5):1213-1218. (in Chinese)[6] 张友旺. 基于动态递归模糊神经网络的自适应电液位置跟踪系统[J]. 控制理论与应用,2005,22(4):551-555. ZHANG Y W.Adaptive electro_bydraulic position tracking system based on dynamic recurrent fuzzy neural network[J].Control Theory and Applications,2005,22(4):551-555. (in Chinese)[7] KARNIK N N,MENDEL J M,LIANG Q.Type-2 fuzzy logic systems[J]. IEEE Trans. Fuzzy Systems, 1999,7(6):643-658.[8] MENDEL J M. Type-2 fuzzy sets made simple[J]. IEEE Trans. Fuzzy Systems, 2002,10(2):117-127.[9] JOHN R,COUPLAND S. Type-2 fuzzy logic: A historical view[J]. IEEE Comput.Intell.Mag.,2007,2(1):57-62.[10] ZENG J, LIU Z Q. Type-2 fuzzy hidden Markov models and their application to speech recognition[J]. IEEE Trans. Fuzzy Syst., 2006,14(3):454-467.[11] JUANG C F,CHIU S H,CHANG S W. A Self-organizing TS-type fuzzy network with support vector learning and its application to classification problems[J]. IEEE Trans, Fuzzy Syst, 2007,15(5):998-1008.[12] JUANG CH F. A self-Evolving Internal Type-2 Fuzzy Neural Network With Online Structure and Parameter learning[J]. IEEE Trans,Fuzzy Syst, 2008,16(6):1411-1424.

Views

599

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design and application of imaging spectrometer with convex grating

Fabrication of triblock liquid crystal elastomer by holography

Aberration correction of a large aperture optical imaging system by holography

Recent Trends of Engineering Optics

Related Author

LIU Yu-juan

CUI Ji-cheng

BAYANHESHIG

CHEN Shao-jie

QI Xiang-dong

TANG Yu-guo

SONG Jing

LI Hong-liang

Related Institution

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences

Graduate University of Chinese Academy of Sciences

School of Material Science and Engineering, Changchun University of Science and Technology

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Graduate University of Chinese Academy of Sciences, Beijing 100039, China

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰