自适应光学图像事后重建技术研究进展

鲍华, 饶长辉, 田雨, 等. 自适应光学图像事后重建技术研究进展[J]. 光电工程, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730
引用本文: 鲍华, 饶长辉, 田雨, 等. 自适应光学图像事后重建技术研究进展[J]. 光电工程, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730
Bao Hua, Rao Changhui, Tian Yu, et al. Research progress on adaptive optical image post reconstruction[J]. Opto-Electronic Engineering, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730
Citation: Bao Hua, Rao Changhui, Tian Yu, et al. Research progress on adaptive optical image post reconstruction[J]. Opto-Electronic Engineering, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730

自适应光学图像事后重建技术研究进展

  • 基金项目:
    国家自然科学基金项目资助(11178004,11727805)
详细信息
    作者简介:
    通讯作者: 饶长辉(1971-),男,博士,研究员,主要从事大口径高分辨力光学成像望远镜技术研究和系统研制工作。E-mail:chrao@ioe.ac.cn
  • 中图分类号: O436.3

Research progress on adaptive optical image post reconstruction

  • Fund Project: Supported by the National Science Foundation of China (11178004, 11727805)
More Information
  • 为进一步提高自适应光学系统的成像质量,本文针对目前广泛使用的盲解卷积,相位差法和斑点重建技术开展了深入研究;详细分析了以上三种技术的各自特点、应用场景和处理对象,并结合自适应光学系统的特点,有针对性的加以算法改进;实验采用自适应光学人眼视网膜细胞图像和自适应光学太阳黑子图像进行算法验证,结果表明经改进后的图像处理技术可以有效提高自适应光学图像的质量和分辨力,较好的满足了自适应光学系统对图像事后处理的需求。

  • Overview: The light wave from target is influenced by outside factors such as the atmosphere turbulence, the aberration of telescope and so on. To overcome these problems, the adaptive optical (AO) technique was proposed since 1950s. However, restricted by the accuracy of wave-front sensor, the limited correction of deformable mirror and the limited bandwidth of close-loop, wave-front distortion can only be corrected by AO system partially. Therefore, the AO imaging results are still affected by the residual wave-front aberration. To further improve the quality and resolution of AO images, the image post-processing technique is required.

    As we know, the AO technique can effectively reduce the wave-front distortion, so as to effectively reduce the range of solutions of image restoration. Furthermore, the residual wave-front aberration is important prior information to guide the optimal iteration process. In addition, the image post-processing will be more robust as the AO images have higher peak-to-signal ratio (PSNR). However, on the other side, the AO technique will change the model of atmosphere turbulence and the statistical distribution of residual aberrations. Therefore, image reconstruction algorithms must take the characteristics of AO system into consideration.

    Currently, the major image processing schemes include blind deconvolution (BD), phase diversity (PD) and speckle imaging technologies (SI). BD is one of the most flexible technologies without special requirements for imaging system and processing object, but BD needs prior knowledge about PSF and support region of real targets to restrict the solving procedure. PD is an aberration detection based on image restorion technology, by using a few groups of images acquired from the same object with different optical channels simultaneously. The main challenge of PD is that this technology requires an extra set of imaging equipment, and the algorithm is sensitive to parameters. SI technology uses the statistical characteristics of atmosphere turbulence to reconstruct the phase and amplitude of the imaging target respectively, which has widely been applied to high resolution solar image reconstruction. However, as SI is based on the statistical information of atmosphere turbulence, it needs hundreds of short-exposure images to reconstruct a single image; therefore the imaged object cannot have obviously changing in the imaging procedure.

    In order to get acceptable reconstructed AO images, the major three image processing technologies mentioned above have been deeply discussed in this paper, and relevant improvements are proposed to suit AO system characteristics. The high quality processing results of human retinal images and the large field of view of sunspots images have proved our methods are effective and reliable.

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  • 图 1  多帧迭代盲解卷积算法流程图

    Figure 1.  Flow chart of multi-frame iterated blind deconvolution

    图 2  人眼视网膜图像的盲解卷积复原。(a) AO闭环校正后的图像;(b)盲解卷积复原图像

    Figure 2.  Retinal image restoration. (a) Degenerated image obtained by AOSLO; (b) Corresponding image reconstructed by blind deconvolution

    图 3  单CCD相位差法成像装置光路结构

    Figure 3.  Optical path structure of single CCD phase diversity imaging device

    图 4  相位差法系统离焦量光学测量算法流程图

    Figure 4.  Flow chart of optical measurement algorithm for defocus

    图 5  太阳黑子AO图像PD重建结果。(a)自适应光学图像;(b) PD重建图像;(c) PD探测相差

    Figure 5.  Phase diversity reconstruction. (a) Sunspot image from AO system; (b) Corresponding image restored by PD; (c) Wavefront residual aberration restored by PD

    图 6  斑点图像重建算法流程图

    Figure 6.  Flow chart of speckle image reconstruction algorithm

    图 7  (a) 自适应光学系统图像;(b)斑点重建图像

    Figure 7.  (a) Image after AO correction; (b) Image reconstructed by speckle reconstruction

    图 8  自适应光学系统图像与斑点重建图像相同子区域对比度

    Figure 8.  (a) Comparison of different subareas in AO corrected image; (b) Comparison of corresponding subareas in image reconstructed by speckle reconstruction algorithm

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出版历程
收稿日期:  2017-12-27
修回日期:  2018-02-07
刊出日期:  2018-03-15

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