全自动尿沉渣检验仪自动调焦方法研究
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摘要
自动调焦技术是广泛应用于数字成像系统及各种光学仪器设备中的关键技术。对于如全自动尿沉渣检验仪这类全自动光学仪器而言,自动调焦通常是必备的基本功能。自动调焦的精确度直接关系到所成图像的质量优劣,并进而影响之后图像处理任务的难易和结果。对自动调焦方法进行研究,具有重要的理论与应用意义。
绪论部分对自动调焦技术的历史发展进行了简要的回顾,概要地介绍了常见自动调焦方法所采用的技术路线,说明了自动调焦技术的优越性,并阐明了论文研究课题的应用背景及重要性；
然后对基于图像处理的已有自动调焦评价函数进行了详细的介绍,主要包括频域法、熵函数法和微分算子法等方法,同时对这些方法的优缺点进行了一定的分析；
根据对已有方法缺点的分析,在第三章和第四章中,分别提出了两种新的调焦评价函数。第三章所提出的方法首先利用边缘检测来提取被成像对象的大致轮廓,然后通过考察轮廓上各像素点处的梯度强弱来判断图像清晰与否,而忽略了已有评价函数中对象或背景内部的灰度变化对评价值的贡献,从而更符合人类视觉对清晰度的感知；
第四章的解决思路也与之类似,但相较于采用较为复杂耗时的边缘检测方法,第四章提出的方法则是通过将图像加以分块之后,利用各个图像块中最大的一个或少数几个梯度值作为该图像块中梯度的代表值,来求取梯度和作为清晰度的评价值,从而屏蔽掉绝大多数对象或背景内部梯度对评价值的影响；两种方法均在实际图像上与若干已有评价函数进行了对比实验,实验结果表明所提出的方法在调焦精度上优于已有方法；
第五章对调焦过程中清晰度峰值的搜索问题进行了考察。首先介绍了Fibonacci搜索算法、函数逼近法、爬山搜索算法等已有方法。在考虑实时性要求的基础上,采用了改进的变步长爬山搜索算法。由于所提出的评价函数计算较为费时,难以满足自动调焦的实时性要求,因此在变步长爬山搜索算法的基础上,提出了一种粗调与细调相结合的调焦过程控制方法,先采用准确性较差但计算迅速的Roberts梯度算子来获取粗调评价函数,大致确定了清晰度峰值所在的小调焦范围之后,再利用提出的比较费时但精度更高的评价函数进行细调,以获得最佳的速度-精度综合性能。
Auto-focusing is a crucial technology broadly applied in digital imaging systems and various optical instruments and devices. For fully automated optical instruments such as automatic urinary sediment analyzer, auto-focusing is one of the necessary fundamental functions. The accuracy of auto-focusing directly influences the quality of the obtained images, and subsequently the hardship and results of the following image processing tasks. Researches on auto-focusing are thus of significant theoretical and practical importance.
In the review of this thesis, the development history of the auto-focusing technology is briefly summarized, a concise introduction on common auto-focusing techniques is given, the advantages of auto-focusing are commented, and the application background and the importance of the proposed research are explained.
Existing focus measures based on image processing techniques such as methods using frequency domain information, entropy-based methods and derivative-operator-based methods are then introduced in details. Advantages and disadvantages of the methods are analyzed.
With the conclusions drawn from the analysis on the disadvantages of the existing focus measures, two new focus measures are proposed in Chapter 3 and 4, respectively. The method given in Chapter 3 uses edge detection to extract the rough contours of the imaged objects, and the definition of the image is judged by the image gradients along the contours. In this way, the image gradients within object or background regions, which contribute to the focus measure in the existing methods, are discarded, and the new focus measure is thus more compatible with the human perception of the image definition.
The other method described in Chapter 4 follows a similar idea. However, instead of using the time-consuming edge detection, the proposed focus measure first partitions the image into square sub-image blocks, and the most significant image gradient(s) of each block are obtained as the representative of the image gradients in the block, and the sum of these representative image gradients gives the final focus measure. Impacts of image gradients in object and background regions on the focus measure is also filtered out in this approach. Both new focus measures are experimented on real world images in comparisons with several existing focus measures, and the results show that the proposed methods are advantageous over the existing ones.
The search of the optimal image definition during a focusing process is inspected in Chapter 5. Existing approaches such as Fibonacci searching, function approximation and hill-climbing algorithm are introduced. Considering the real-time requirements on the auto-focusing apparatus, a hill-climbing algorithm with variable step length is employed. Since the focus measures proposed in Chapter 3 and 4 are computationally expensive and the real-time requirement on auto focusing is difficult to fulfill by directly applying the new focus measures, a coarse-to-fine focusing strategy based on the variable step length hill-climbing method is given. In this strategy, a less accurate but very fast coarse focus measure utilizing the Roberts operator is first applied to estimate the approximate focusing range where the optimal definition is located. The more time-consuming but more accurate new focus measure is then used to do a finer search in this small range to determine the best focusing, and thus to obtain the optimal speed-accuracy performance.
绪论部分对自动调焦技术的历史发展进行了简要的回顾,概要地介绍了常见自动调焦方法所采用的技术路线,说明了自动调焦技术的优越性,并阐明了论文研究课题的应用背景及重要性；
然后对基于图像处理的已有自动调焦评价函数进行了详细的介绍,主要包括频域法、熵函数法和微分算子法等方法,同时对这些方法的优缺点进行了一定的分析；
根据对已有方法缺点的分析,在第三章和第四章中,分别提出了两种新的调焦评价函数。第三章所提出的方法首先利用边缘检测来提取被成像对象的大致轮廓,然后通过考察轮廓上各像素点处的梯度强弱来判断图像清晰与否,而忽略了已有评价函数中对象或背景内部的灰度变化对评价值的贡献,从而更符合人类视觉对清晰度的感知；
第四章的解决思路也与之类似,但相较于采用较为复杂耗时的边缘检测方法,第四章提出的方法则是通过将图像加以分块之后,利用各个图像块中最大的一个或少数几个梯度值作为该图像块中梯度的代表值,来求取梯度和作为清晰度的评价值,从而屏蔽掉绝大多数对象或背景内部梯度对评价值的影响；两种方法均在实际图像上与若干已有评价函数进行了对比实验,实验结果表明所提出的方法在调焦精度上优于已有方法；
第五章对调焦过程中清晰度峰值的搜索问题进行了考察。首先介绍了Fibonacci搜索算法、函数逼近法、爬山搜索算法等已有方法。在考虑实时性要求的基础上,采用了改进的变步长爬山搜索算法。由于所提出的评价函数计算较为费时,难以满足自动调焦的实时性要求,因此在变步长爬山搜索算法的基础上,提出了一种粗调与细调相结合的调焦过程控制方法,先采用准确性较差但计算迅速的Roberts梯度算子来获取粗调评价函数,大致确定了清晰度峰值所在的小调焦范围之后,再利用提出的比较费时但精度更高的评价函数进行细调,以获得最佳的速度-精度综合性能。
Auto-focusing is a crucial technology broadly applied in digital imaging systems and various optical instruments and devices. For fully automated optical instruments such as automatic urinary sediment analyzer, auto-focusing is one of the necessary fundamental functions. The accuracy of auto-focusing directly influences the quality of the obtained images, and subsequently the hardship and results of the following image processing tasks. Researches on auto-focusing are thus of significant theoretical and practical importance.
In the review of this thesis, the development history of the auto-focusing technology is briefly summarized, a concise introduction on common auto-focusing techniques is given, the advantages of auto-focusing are commented, and the application background and the importance of the proposed research are explained.
Existing focus measures based on image processing techniques such as methods using frequency domain information, entropy-based methods and derivative-operator-based methods are then introduced in details. Advantages and disadvantages of the methods are analyzed.
With the conclusions drawn from the analysis on the disadvantages of the existing focus measures, two new focus measures are proposed in Chapter 3 and 4, respectively. The method given in Chapter 3 uses edge detection to extract the rough contours of the imaged objects, and the definition of the image is judged by the image gradients along the contours. In this way, the image gradients within object or background regions, which contribute to the focus measure in the existing methods, are discarded, and the new focus measure is thus more compatible with the human perception of the image definition.
The other method described in Chapter 4 follows a similar idea. However, instead of using the time-consuming edge detection, the proposed focus measure first partitions the image into square sub-image blocks, and the most significant image gradient(s) of each block are obtained as the representative of the image gradients in the block, and the sum of these representative image gradients gives the final focus measure. Impacts of image gradients in object and background regions on the focus measure is also filtered out in this approach. Both new focus measures are experimented on real world images in comparisons with several existing focus measures, and the results show that the proposed methods are advantageous over the existing ones.
The search of the optimal image definition during a focusing process is inspected in Chapter 5. Existing approaches such as Fibonacci searching, function approximation and hill-climbing algorithm are introduced. Considering the real-time requirements on the auto-focusing apparatus, a hill-climbing algorithm with variable step length is employed. Since the focus measures proposed in Chapter 3 and 4 are computationally expensive and the real-time requirement on auto focusing is difficult to fulfill by directly applying the new focus measures, a coarse-to-fine focusing strategy based on the variable step length hill-climbing method is given. In this strategy, a less accurate but very fast coarse focus measure utilizing the Roberts operator is first applied to estimate the approximate focusing range where the optimal definition is located. The more time-consuming but more accurate new focus measure is then used to do a finer search in this small range to determine the best focusing, and thus to obtain the optimal speed-accuracy performance.
引文
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[10]马彦平,白由路,高祥照,等.计算机视觉技术在农业生产中的应用与展望.中国农业资源与区划.2009,30(4)：20-27
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