Abstract
Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.
Highlights
Optical microscope [1, 2] plays an important role in human observation and understanding of the micro world
It is a common task for all the subjects applying computer vision automatic detection system to realize the good autofocusing performance of optical microscope. e traditional autofocus technology is studied from the perspective of focal length measurement, and the new computer-controlled microscope autofocus based on image processing is a multidisciplinary comprehensive application combining machine vision, image processing, optimization theory, and electromechanical technology
With the addition of noise, whether the wavelet transform is improved or not, the curves of both have a certain degree of floating up, but, in this paper, the curve floating up degree of the improved algorithm of wavelet transform is not high, the side lobe value rising is not serious, and the traditional wavelet transform floating up degree is more serious; the side lobe value rising is far more than the improved algorithm. is phenomenon shows that the improvement of wavelet transform in this paper can improve the antinoise performance of the algorithm, which is conducive to its application in image definition evaluation
Summary
Optical microscope [1, 2] plays an important role in human observation and understanding of the micro world. In the automatic detection system of computer vision, it is one of the key technologies to realize the automatic focusing of microscope [3]. Autofocus refers to the process in which the system automatically adjusts the mechanical structure (image distance or object distance) to make the image clear again when the image is blurred due to defocusing. It is a common task for all the subjects applying computer vision automatic detection system to realize the good autofocusing performance of optical microscope.
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