激光共焦扫描显微镜论文：共焦显微图像复原及其正则化方法研究.doc

激光共焦扫描显微镜论文：共焦显微图像复原及其正则化方法研究【中文摘要】激光共焦扫描显微镜作为一种具有高纵深分辨率的现代显微成像设备,是生物医学、生命科学等领域研究的强有力工具。但是,由于成像系统衍射和噪声干扰等因素,所采集到的显微图像不可避免地存在噪声和模糊,使图像的质量下降。图像复原技术作为基于图像处理技术的后处理手段,是减轻噪声和成像模糊,恢复原始图像的有效途径。正则化方法是通过引入一定的约束项将图像复原由不适定问题转换成适定问题,是解决图像复原这一不适定问题的一种有效方法。因此,本文针对共焦显微图像的复原及其正则化方法进行了深入研究。主要研究工作如下:(1)对传统的维纳滤波图像复原算法进行了研究,并将原方法中的固定k值改进为自适应的k值。实验证明,相对于维纳滤波算法,本文方法复原后的图像细节更加清晰,整体的视觉效果更好。(2)对传统NAS-RIF图像复原算法进行了研究,加入正则化参数,改进了NAS-RIF算法,实验证明,改进后的算法复原的图像细节清楚度有所提高,可较好实现显微图像的复原。(3)对传统R-L图像复原算法进行了研究,结合最大熵正则化方法改进了R-L算法。实验证明,改进后的算法复原出来的图像基本上消除了原始图像的模糊现象,恢复出了稍多的目标细节,但出现了很多微小结构假象。(4)对传统小波图像复原算法进行了研究,结合频域正则化方法改进了小波图像复原算法。实验证明,改进后的算法复原的图像PSNR指标和视觉效果较优。模拟图像和实际的激光共焦扫描显微图像的实验结果均表明:与传统的复原算法相比,提出的改进算法的图像复原效果较好,峰值信噪比和复原后的视觉效果较优,为深入开展生物显微图像的分析工作提供了有效的技术手段。【英文摘要】As modern microscopic imaging equipment, confocal laser scanning microscope,with its high depth resolution,is a powerful tool for biomedical and life sciences and other fields. However, in confocal laser scanning microscope imaging acquisition process, due to diffraction and imaging system noise and other factors, the quality of the image has been inevitably degraded. Post-processing image restoration technology, as means to eliminate noise and image blur, are an effective way of restoring sample fluorescent objects. Regularization method introduced certain constraints to convert ill-posed image into well-posed image. It is an effective method of solving the ill-posed image restoration problem.Therefore,in this paper, we present in-depth study of confocal laser scanning fluorescence microscopy image restoration method The main work is listed as follows:(1)The traditional Wiener filtering image restoration algorithm is studied, and proposed a new way of using adaptive value instead of fixed value of the original method. Simulation results show that, compared to Wiener filter algorithm, this method restored image detail more clearly, the overall visual effect is better(2)By adding the regularization parameter in traditional NAS-RIF image restoration algorithm, we improved NAS-RIF algorithm. Experiments show that, the improved algorithm increased clarity of image detail ,it can achieve better recovery of microscopic image(3)Studied the traditional R-L image restoration algorithm, using maximum entropy regularization method for improving the R-L algorithm. Experiments show that the improved algorithm basically eliminated blur in original image and restore more detail of target, but there have been many false impression of tiny structures.(4)Studied the wavelet image restoration algorithm, adopting frequency-domain regularization method for improving the wavelet image restoration algorithm. Experiments show that the improved algorithm of image restoration is better in visual indicators and PSNR.Experiments of simulated images and real confocal laser scanning microscope image. results show that the improved algorithm for image restoration was better than the original algorithm in peak signal to noise ratio and optimum visual effect. It created the conditions for analysis of biological microscopic images.【关键词】激光共焦扫描显微镜 图像复原图 正则化 小波变换【英文关键词】confocal laser scanning microscopy image restoration regularization wavelet transform【目录】共焦显微图像复原及其正则化方法研究摘要4-5Abstract51 绪论10-161.1 引言10-111.2 共焦显微图像的退化及图像复原11-121.3 国内外研究现状12-131.3.1 图像复原及其正则化技术121.3.2 共焦显微图像复原的方法12-131.4 论文的结构及主要研究内容13-162 共焦显微图像复原的理论基础16-262.1 成像系统的一般退化模型16-172.2 共焦显微图像的退化模型17-182.3 图像复原中的病态特征18-192.4 图像复原中的正则化处理19-202.5 图像复原质量的评价方法202.6 几类常用的图像复原算法20-242.6.1 线性反卷积20-212.6.2 非线性约束迭代反卷积21-222.6.3 统计反卷积22-232.6.4 盲反卷积23-242.7 本章小结24-263 改进的 Wiener 滤波图像复原算法26-323.1 Wiener 滤波原理26-273.2 Wiener 滤波算法的改进27-283.3 实验结果与分析28-313.4 本章小结31-324 基于 NAS-RIF 算法的正则化图像复原算法32-424.1 NAS-RIF 算法简介324.2 NAS-RIF 图像复原算法的原理32-354.3 激光共焦显微图像复原 NAS-RIF 算法的改进35-404.3.1 偏微分去噪预处理354.3.2 正则化代价函数35-374.3.3 实验结果与分析37-404.4 本章小结40-425 基于 R-L 图像复原的正则化方法42-505.1 Richardson-Lucy 的原理42-435.2 激光共焦显微图像复原 Richardson-Lucy 算法的改进43-465.2.1 偏微分去噪预处理435.2.2 最大熵正则化43-445.2.3 R-L 最大熵正则化44-465.3 试验结果与分析46-495.4 本章小结49-506 基于小波变换的正则化图像复原算法50-606.1 小波变换基本理论50-526.1.1 连续小波变换50-516.1.2 离散小波变换516.1.3 二维信号的小波多分辨率分析51-526.