Automatic Focusing Method of Microscopes Based on Image Processing

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.

Automated Cell Counting using Image Processing

“…This highlights the importance of getting highly focused images. Indeed, modern microscopes are commonly fitted with auto-focus as standard and there exists different algorithms that may be used to detect blurriness in microscopic images [30]. These algorithms can be combined with the proposed method to give a warning to users of possible errors in cell counting due to the blurring of images.…”

Study of Microscope Autofocus Based on Weighted Light Intensity

“…These approaches can be categorized into the following groups based on the principles applied. 1) Analyzing images acquired using software algorithm to determine the best focus [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]; 2) based on auxiliary laser focusing spot deviation or spot diameter variation [21][22][23][24].…”

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

“…[2], [12]- [14] Building upon prior research, including the Microscope Modification with Digital Magnification Using a Camera System in 2018, the current study introduces the Microscope Camera to Increase Accuracy of Counted Mycobacterium Tuberculosis on Acid-Fast Bacteria Sputum by Image Processing of Thresholding Method. [15]- [17][18] [15], [19], [20] This innovative tool, designed as a digital microscope, not only addresses existing challenges in TB detection but also introduces a practical solution. The tool facilitates the examination of ZIEHL NELSSEN-stained TB samples, allowing analysts to visualize results on an external monitor, thereby enhancing accuracy in TB bacteria counting.…”