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Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Published online by Cambridge University Press:  01 March 2021

Usman Ali  and
Muhammad Tariq Mahmood Open the ORCID record for Muhammad Tariq Mahmood [Opens in a new window]
Usman Ali
Affiliation:
Computer Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600, Chungjeol-ro, Byeongcheon-myeon, 31253Cheonan, South Korea
Muhammad Tariq Mahmood*
Affiliation:
Computer Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600, Chungjeol-ro, Byeongcheon-myeon, 31253Cheonan, South Korea Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600, Chungjeol-ro, Byeongcheon-myeon, 31253Cheonan, South Korea
*
*Author for correspondence: Muhammad Tariq Mahmood, E-mail: tariq@koreatech.ac.kr
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Abstract

In shape-from-focus (SFF) methods, a single focus measure is used to compute the focus volume. However, it seems that a single focus measure operator is not capable of computing accurate focus values for the images of diverse types of object shapes. Furthermore, most of the SFF methods try to improve the depth map without considering any additional structural or prior information. Consequently, the extracted shape of the object might lack important details. In this work, we address these problems and suggest a method in which depth hypotheses are combined for a more accurate 3D shape through 3D weighted least squares. First, depth hypotheses are obtained by applying a number of focus operators. Then, structural prior or guidance volume is extracted from the focus measure volumes. Finally, a 3D weighted least squares optimization technique is applied to the depth hypothesis volume, where weights are computed from the guidance volume. Thus, by inducing structural prior, an improved resultant depth map is obtained. The proposed method was tested using various image sequences of synthetic and microscopic real objects. Experimental results and comparative analysis demonstrated the effectiveness of the proposed method.

Keywords

3D weighted least squaresdepth fusionfocus measure operatorguidance mapregularizationshape from focus
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 27 , Issue 2 , April 2021 , pp. 344 - 356
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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