基于图像技术的自动调焦方法研究
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摘要
作为光学成像系统的关键技术之一，自动调焦技术已成功应用于空间光学遥感系统、靶场光学测量系统、自动监控系统、显微分析系统以及各种数码相机和摄像系统等。在一个成像系统中，准确聚焦是排在所有其他功能之前需要解决的首要问题，其最终效果将直接影响系统的成像质量及后续图像处理和应用的有效性。本文针对现有自动调焦方法存在的问题，研究了基于图像技术的两类自动调焦方法：离焦深度(DFD)法和聚焦深度(DFF)法，主要就基于图像复原的离焦深度法、基于模糊量估计的离焦深度法以及聚焦深度法中调焦评价函数的模型建立、调焦窗口的智能选取、调焦搜索策略的设计等问题进行了深入研究，并设计了相应的自动调焦系统实验平台。本文的主要工作如下：
1、分析了离焦模糊图像复原算法中常用的两种点扩散函数模型：圆盘离焦模型和高斯离焦模型，由于这两种模型均不能准确逼近实际的离焦点扩散函数,导致离焦图像复原无法取得预期效果。分析了非负支撑域受限递归逆滤波(NAS-RIF)算法存在的缺点，提出了一种基于提升小波变换的NAS-RIF离焦模糊图像盲复原算法。该算法能够在有效抑制图像噪声的同时，较好地恢复离焦图像的边缘细节，实现了利用单幅图像来完成准焦位置图像的最佳恢复。
2、研究了光学成像系统的离焦模型，提出了一种基于离焦深度的自动调焦方法。在仅改变光学镜头位置的条件下，应用成像系统离焦模型，推导了以模糊差异为参量的离焦深度的计算公式。采用空间域的卷积/反卷积变换求出两幅不同程度离焦图像的模糊差异，并利用推导得到的公式计算出目标物体的深度信息，从而调节镜头位置完成自动调焦。该方法对成像系统和场景均没有过多要求，适用于任意目标物体。
3、分析了现有的基于图像梯度、频域、信息学、统计学和小波分析的调焦评价函数。基于生物视觉机理，利用小波变换与人类视觉系统(HVS)特性的多通道特性相匹配的特点，结合对比敏感度函数的带通特性，提出了一种基于HVS加权的小波调焦评价函数，既可以客观准确地评价调焦过程中的离焦图像和聚焦图像，又符合人眼的视觉感知特性。选取陡峭区宽度、陡峭度、清晰度比率、局部极值因子和灵敏度等作为评价指标，通过与其它几种常用调焦评价函数进行比较，分析了基于HVS加权小波调焦评价函数的性能。
4、研究了调焦窗口的选择对调焦效果的影响，分别分析了调焦窗口的大小和位置对自动调焦的影响。分析了传统的调焦窗口选择方法只能选取固定区域，而不能很好地适用于成像主体位置变化的缺陷。针对成像主体位置不固定的情况，提出了一种基于自适应遗传算法的最优调焦窗口选择法，有效地减少了成像主体不在中心位置时引起的聚焦失败。
5、分析了常用的调焦搜索算法在实际工程应用中存在的问题。针对爬山法搜索效率低的缺点，提出了一种基于自组织特征映射神经网络的搜索算法。该算法避免了电机在最佳聚焦位置附近的往返运动，有效地提高了搜索效率。
6、针对提出的自动调焦方法，设计了自动调焦系统实验平台，确认了相应的软件实现方案，并进行了一系列的调焦实验。根据测试的实验结果，对自动调焦的精度、实时性和稳定性做了详细的分析。实验结果表明提出的自动调焦方法具有较高的可行性，为实际工程应用提供了解决方案。
As a key technology in the optical imaging system, the autofocusing technologyhas been successfully used in the space optical remote sensing system, the rangeoptical measurement system, the automatic monitoring system, the microscopyanalysis system, a variety of digital cameras system, and so on. In an imagingsystem, accurate focusing of the optical lens is the most important issue that needs tobe solved before all other functions. The final results of autofocusing directly impacton the imaging quality and the effectiveness of subsequent imaging processing andits applications. In order to solve the problems of the existing autofocusing methods,two kinds of autofocusing method, including depth from defocus (DFD) and depthfrom focus (DFF) based on image technology, are studied. Defocus from defocusalgorithm based on image restoration, defocus from defocus algorithm based onestimating blur amount, as well as the problems of the depth from focus method,such as focus measure function modeling, the smart selection of focus window, thedesign of focus searching strategy, are also deeply studied. Moreover, theexperimental platform for autofocusing system has been designed. The maincontributions of this dissertation can be summarized as follows:
1. Two kinds of point spread function models, including the disc defocus modeland the Gaussian defocus model, commonly used in the defocus blurred imagerestoration algorithms are analyzed. Since these two models can not accurately approximate to the actual defocus point spread function, leading to the defocusedimage restoration algorithms are incapable of achieving the desired results. Theshortcoming of the non-negativity and support constraints recursive inverse filtering(NAS-RIF) algorithm is analyzed, and a NAS-RIF blind algorithm for defocusblurred image based on lifting wavelet transform is proposed. This algorithm notonly can restrain the noise effectively, but also can restore the detail edges of thedefocus images. In other words, the best restoration of defocus images is acquiredby using a single blurred image.
2. The defocus model of the imaging system is analyzed. The presentation of theautofocusing algorithm based on DFD is provided. Under the condition of onlychanging lens position of imaging system, a formula to compute the depth fromdefocus of target with respect to the blur difference is derived according to thedefocus model of imaging system. A Spatial-Domain Convolution/DeconvolutionTransform Method (STM) is applied to estimate the blur difference between the twodifferent defocus images, and then the depth information of target is computed withthe derived formula. Consequently, the lens could be adjusted to accomplishautofocusing. This method has no excessive requirements on the imaging systemand imaging target, so it is applicable to any target.
3. The study of the existing focus measure functions based on image gradient,frequency domain, informatics, statistics and wavelet analysis are performed. On thebasis of the mechanism of biological vision, with the feature of wavelet transformmatched to the multi-channel characteristic of the human vision system (HVS),combined with a band-pass characteristic of contrast sensitivity function, a weightedwavelet focus measure function is proposed based on HVS. This function not onlycan objectively and accurately evaluate the defocused images and the focusedimages in the process of autofocusing, but also can satisfy the perceptual property ofhuman eye. The width of steep part of focus measure curve, the steepness, the ratioof sharpness, and the factor of local extreme and the sensitivity are selected to beevaluation indexes. And then the characteristic of the weighted wavelet focus measure function based on HVS is analyzed by comparing with several other focusmeasure functions.
4. The influence on the autofocusing effect of the focus window selection isstudied. The influence of focus window size and focus window position toautofocusing are analyzed, respectively. The limitations of the traditional focuswindow selection methods, only selecting fixed focus areas and not well-suited forthe change of the target position, are analyzed. For the case of the target position isnot fixed, a focus window selection method is presented based on self-adaptivegenetic algorithm (GA). This method can effectively reduce the focusing failure dueto the target not located in the center of the imaging field.
5. The existing issues of the current focus search algorithms in practicalengineering applications are analyzed. In order to overcome the disadvantage of lowsearch efficiency of the mountain climbing search algorithm, an improved searchingalgorithm is presented based on the self-organizing map (SOM) neural network.This algorithm can significantly accelerate the search speed because it can avoidredundant movement of the motor around the best focused lens position.
6. Aiming at the proposed autofocusing method, the experimental platform forautofocusing system has been designed, and its implementation is also proposed.Subsequently, series of autofocusing experiments are carried out. According to theexperimental results of the test, the detailed analyses of the accuracy, real-time andstability of autofocusing are made. The experimental results demonstrate that theproposed autofocusing method has a high feasibility and can provide a solution forpractical applications.
1、分析了离焦模糊图像复原算法中常用的两种点扩散函数模型：圆盘离焦模型和高斯离焦模型，由于这两种模型均不能准确逼近实际的离焦点扩散函数,导致离焦图像复原无法取得预期效果。分析了非负支撑域受限递归逆滤波(NAS-RIF)算法存在的缺点，提出了一种基于提升小波变换的NAS-RIF离焦模糊图像盲复原算法。该算法能够在有效抑制图像噪声的同时，较好地恢复离焦图像的边缘细节，实现了利用单幅图像来完成准焦位置图像的最佳恢复。
2、研究了光学成像系统的离焦模型，提出了一种基于离焦深度的自动调焦方法。在仅改变光学镜头位置的条件下，应用成像系统离焦模型，推导了以模糊差异为参量的离焦深度的计算公式。采用空间域的卷积/反卷积变换求出两幅不同程度离焦图像的模糊差异，并利用推导得到的公式计算出目标物体的深度信息，从而调节镜头位置完成自动调焦。该方法对成像系统和场景均没有过多要求，适用于任意目标物体。
3、分析了现有的基于图像梯度、频域、信息学、统计学和小波分析的调焦评价函数。基于生物视觉机理，利用小波变换与人类视觉系统(HVS)特性的多通道特性相匹配的特点，结合对比敏感度函数的带通特性，提出了一种基于HVS加权的小波调焦评价函数，既可以客观准确地评价调焦过程中的离焦图像和聚焦图像，又符合人眼的视觉感知特性。选取陡峭区宽度、陡峭度、清晰度比率、局部极值因子和灵敏度等作为评价指标，通过与其它几种常用调焦评价函数进行比较，分析了基于HVS加权小波调焦评价函数的性能。
4、研究了调焦窗口的选择对调焦效果的影响，分别分析了调焦窗口的大小和位置对自动调焦的影响。分析了传统的调焦窗口选择方法只能选取固定区域，而不能很好地适用于成像主体位置变化的缺陷。针对成像主体位置不固定的情况，提出了一种基于自适应遗传算法的最优调焦窗口选择法，有效地减少了成像主体不在中心位置时引起的聚焦失败。
5、分析了常用的调焦搜索算法在实际工程应用中存在的问题。针对爬山法搜索效率低的缺点，提出了一种基于自组织特征映射神经网络的搜索算法。该算法避免了电机在最佳聚焦位置附近的往返运动，有效地提高了搜索效率。
6、针对提出的自动调焦方法，设计了自动调焦系统实验平台，确认了相应的软件实现方案，并进行了一系列的调焦实验。根据测试的实验结果，对自动调焦的精度、实时性和稳定性做了详细的分析。实验结果表明提出的自动调焦方法具有较高的可行性，为实际工程应用提供了解决方案。
As a key technology in the optical imaging system, the autofocusing technologyhas been successfully used in the space optical remote sensing system, the rangeoptical measurement system, the automatic monitoring system, the microscopyanalysis system, a variety of digital cameras system, and so on. In an imagingsystem, accurate focusing of the optical lens is the most important issue that needs tobe solved before all other functions. The final results of autofocusing directly impacton the imaging quality and the effectiveness of subsequent imaging processing andits applications. In order to solve the problems of the existing autofocusing methods,two kinds of autofocusing method, including depth from defocus (DFD) and depthfrom focus (DFF) based on image technology, are studied. Defocus from defocusalgorithm based on image restoration, defocus from defocus algorithm based onestimating blur amount, as well as the problems of the depth from focus method,such as focus measure function modeling, the smart selection of focus window, thedesign of focus searching strategy, are also deeply studied. Moreover, theexperimental platform for autofocusing system has been designed. The maincontributions of this dissertation can be summarized as follows:
1. Two kinds of point spread function models, including the disc defocus modeland the Gaussian defocus model, commonly used in the defocus blurred imagerestoration algorithms are analyzed. Since these two models can not accurately approximate to the actual defocus point spread function, leading to the defocusedimage restoration algorithms are incapable of achieving the desired results. Theshortcoming of the non-negativity and support constraints recursive inverse filtering(NAS-RIF) algorithm is analyzed, and a NAS-RIF blind algorithm for defocusblurred image based on lifting wavelet transform is proposed. This algorithm notonly can restrain the noise effectively, but also can restore the detail edges of thedefocus images. In other words, the best restoration of defocus images is acquiredby using a single blurred image.
2. The defocus model of the imaging system is analyzed. The presentation of theautofocusing algorithm based on DFD is provided. Under the condition of onlychanging lens position of imaging system, a formula to compute the depth fromdefocus of target with respect to the blur difference is derived according to thedefocus model of imaging system. A Spatial-Domain Convolution/DeconvolutionTransform Method (STM) is applied to estimate the blur difference between the twodifferent defocus images, and then the depth information of target is computed withthe derived formula. Consequently, the lens could be adjusted to accomplishautofocusing. This method has no excessive requirements on the imaging systemand imaging target, so it is applicable to any target.
3. The study of the existing focus measure functions based on image gradient,frequency domain, informatics, statistics and wavelet analysis are performed. On thebasis of the mechanism of biological vision, with the feature of wavelet transformmatched to the multi-channel characteristic of the human vision system (HVS),combined with a band-pass characteristic of contrast sensitivity function, a weightedwavelet focus measure function is proposed based on HVS. This function not onlycan objectively and accurately evaluate the defocused images and the focusedimages in the process of autofocusing, but also can satisfy the perceptual property ofhuman eye. The width of steep part of focus measure curve, the steepness, the ratioof sharpness, and the factor of local extreme and the sensitivity are selected to beevaluation indexes. And then the characteristic of the weighted wavelet focus measure function based on HVS is analyzed by comparing with several other focusmeasure functions.
4. The influence on the autofocusing effect of the focus window selection isstudied. The influence of focus window size and focus window position toautofocusing are analyzed, respectively. The limitations of the traditional focuswindow selection methods, only selecting fixed focus areas and not well-suited forthe change of the target position, are analyzed. For the case of the target position isnot fixed, a focus window selection method is presented based on self-adaptivegenetic algorithm (GA). This method can effectively reduce the focusing failure dueto the target not located in the center of the imaging field.
5. The existing issues of the current focus search algorithms in practicalengineering applications are analyzed. In order to overcome the disadvantage of lowsearch efficiency of the mountain climbing search algorithm, an improved searchingalgorithm is presented based on the self-organizing map (SOM) neural network.This algorithm can significantly accelerate the search speed because it can avoidredundant movement of the motor around the best focused lens position.
6. Aiming at the proposed autofocusing method, the experimental platform forautofocusing system has been designed, and its implementation is also proposed.Subsequently, series of autofocusing experiments are carried out. According to theexperimental results of the test, the detailed analyses of the accuracy, real-time andstability of autofocusing are made. The experimental results demonstrate that theproposed autofocusing method has a high feasibility and can provide a solution forpractical applications.
引文
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