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题名:
波前解卷积及自适应光学图像事后处理技术研究
作者: 田雨
学位类别: 博士
答辩日期: 2009-06-01
授予单位: 中国科学院光电技术研究所
授予地点: 光电技术研究所
导师: 饶长辉
关键词: 图像复原 ; 自适应光学 ; 解卷积
其他题名: Deconvolution from Wavefront Sensing and Adaptive Optics Image Post-processing
学位专业: 信号与信息处理
中文摘要: 对于大型地基光电成像望远镜而言,由于大气湍流的影响,其成像分辨力严重受限。为克服大气湍流对光学系统成像分辨力的限制,通常采用自适应光学技术、图像事后处理技术以及混合处理技术(自适应光学和图像事后处理相结合)等手段进行高分辨力重建。本文主要针对基于波前探测解卷积的图像复原技术以及基于自适应光学图像的混合处理技术进行系统深入地研究。 在波前解卷积研究方面,本文首先提出了以对高频噪声进行抑制的规整化方法,克服了图像复原问题的病态特性,并根据噪声与真实图像在频谱的分布特点,进行区分化地规整化,以保证在噪声抑制的前提下保持图像的低频部分;经过室内实验证明:该规整化方法可以有效地抑制解卷积过程中高频噪声的影响,恢复出达到理论衍射极限分辨率的图像。与维纳逆滤波相比,该规整化方法复原的图像在图像质量、斯特列尔比以及信噪比上均有明显优势。 为进一步在波前解卷积规整化与保持复原图像质量之间取得更好的平衡,经过分析与吸取主流规整化方法的优缺点后,本文首次提出了基于离散小波包分解的规整化方法,该方法结合了复原图像、卷积算子以及噪声分布特性等各方因素设计规整项,不仅抑制了噪声在解卷积时的放大,而且尽可能地保持图像细节信息不丢失。将此方法应用于室内模拟点源实验中,并与基于奇异值分解规整化的维纳逆滤波进行了对比。实验结果表明:该规整化方法可以有效地解决解卷积问题的病态特性,应用该规整化方法所恢复的图像质量明显提高。 在自适应光学图像事后处理方面,本文发展了一种多帧迭代盲解卷积方法处理自适应光学图像。该方法不需要除正性限制与频带限制外的任何先验知识。与经典的盲解卷积算法不同,该方法有明确的收敛性以及高速的收敛速度,并且有较强的适应能力,可以对点状目标以及扩展目标进行处理。该方法已经以云南天文台1.2米自适应光学望远镜采得的单星和双星目标进行了实际验证。结果表明,该方法能够有效地提高自适应光学图像质量,得到达到衍射极限的图像。 针对37单元人眼眼底自适应光学系统,本文分析了在闭环条件下的波前传感器误差,并发展了一种结合波前信息的自适应光学图像联合估计方法。该方法将代表波前信息的点扩散函数与复原图像估计值进行联合迭代,达到估计复原图像与修正点扩散函数的效果。我们以此方法对37单元人眼眼底自适应光学系统所得到的视网膜图像进行了处理,结果证明了该算法的有效性。 本文的研究成果可以用于天体观测以及眼科等光学相关领域的图像高分辨力恢复处理,对该领域研究工作具有重要的指导作用和参考价值。
英文摘要: For astronomical observations, most of the image degradation is caused by random phase variations due to atompshere turbulence that will limit the resolution of a telescope seriously. In order to overcome the aberrations caused by turbulence, adaptive optics, image post-processing technology and hybrid method (adaptive optics + image post-processing) is applied. This paper is trying to investigate the image restoration based on wavefront sensing and hybrid method for adaptive optics images. The key to image restoration based on wavefront sensing is to solve the ill-conditioning. The paper proposed a method to regularize the ill-posed characteristic by restraining the noise in the degraded images.This is done by apply an additional constraint on degraded images during the deconvolution processing.The regularization parameter distinguishes the noise and true image in spectrum and regularizes respectively to preserve the low frequency part of the true image and restrain the noise in high frequency. In order to improve the images quality furthermore, a method based on new regularization with discrete wavelet packet is proposed after investigation of current popular regularization method. The method designs the regularization parameter according to the restored images, convolved operator and the noise distribution. The original intention of the method is trying to balance the quality of images and the noise reduction. Both of the two regularization methods have been tested by images of point sources indoors with aberrations. The results show that the two methods can solve the ill-posed problem effectively and quality of restored images better than those restored by wiener inverse filter. A post-processing method based on frame selection and multi-frames blind deconvolution to improve images partially corrected by adaptive optics is proposed.The appropriate frames which are suitable for blind deconvolution from the recorded AO close-loop frames series are selected by the frame selection technique and then do the multi-frame blind deconvolution. There is no priori knowledge except for the positive constraint in blind deconvolution. It is benefit for the use of multi-frame images to improve the stability and convergence of the blind deconvolution algorithm.The method had been applied in the image restoration of celestial bodies which were observed by 1.2m telescope equipped with 61-element adaptive optical system at Yunnan Observatory, China. The results show that the method can effectively improve the images partially corrected by adaptive optics. Also, a blind deconvolution method combined with wavefront sensing information is proposed to improve the quality of restored images. The method deconvolves the retinal image partially corrected by 37-element adaptive optics system with wavefront sensor data simultaneously acquired by the AO system. The results show the reliability and validity of this method.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.ioe.ac.cn/handle/181551/352
Appears in Collections:光电技术研究所博硕士论文_学位论文

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Recommended Citation:
田雨. 波前解卷积及自适应光学图像事后处理技术研究[D]. 光电技术研究所. 中国科学院光电技术研究所. 2009.
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