Image-based autofocusing algorithm applied in image fusion process for optical microscope

Yen-Chung Wang; Jing-Feng Weng; Po-Hsun Wu; Guo-Hao Lu; Chao-Feng Liu; Pi-Ying Cheng; Chun-Jen Weng; Yu-Hsin Lin; Ting-Ting Chang

doi:10.1117/12.2595324

1 August 2021 Image-based autofocusing algorithm applied in image fusion process for optical microscope

Yen-Chung Wang, Jing-Feng Weng, Po-Hsun Wu, Guo-Hao Lu, Chao-Feng Liu, Pi-Ying Cheng, Chun-Jen Weng, Yu-Hsin Lin, Ting-Ting Chang

Author Affiliations +

Proceedings Volume 11842, Applications of Digital Image Processing XLIV; 1184210 (2021) https://doi.org/10.1117/12.2595324
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

This study proposes a novel passive auto-focusing algorithm which is applied in the optical microscope. The main core of the proposed auto-focusing algorithm is the weight distribution of Gaussian pyramid and the zero weight values in the designed median mask. It is robust for the noise problem. The optical microscope measurement is one of non-contact optical measurement technology. The captured images will be blurred when the depth of the sample exceeds the depth of field in the microscope. The blurred area in the image easily produces the noise. In order to reduce the problems of the image blurring and the noise, the proposed auto-focusing algorithm is applied in the process of image fusion algorithm. The stack of the images is captured by the microscope system which contains the CCD (i.e. Charge-coupled devices) and the PZT (i.e. piezoelectric transducer). The stack of images contains the clear image on the focal plane and the blurred image out of the depth of field. The process of the image fusion algorithm calculates this stack of the images to obtain the 2D fusion image and the 3D profile, simultaneously. The proposed algorithm is compared to the known four autofocusing algorithms. In the experiment results of the 2D fusion image, the proposed algorithm is as good as the four auto-focusing algorithms. Moreover, in the results of 3D profile, the RMSE error of the proposed algorithm is 287.628, which is lower than the other RMSE errors of the four auto-focusing algorithms.

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Yen-Chung Wang, Jing-Feng Weng, Po-Hsun Wu, Guo-Hao Lu, Chao-Feng Liu, Pi-Ying Cheng, Chun-Jen Weng, Yu-Hsin Lin, and Ting-Ting Chang "Image-based autofocusing algorithm applied in image fusion process for optical microscope", Proc. SPIE 11842, Applications of Digital Image Processing XLIV, 1184210 (1 August 2021); https://doi.org/10.1117/12.2595324

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