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30 April 2022 CNN-based realization of Depth from Defocus technique
Mizuki Kaneda, Toshiyuki Yoshida
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Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217724 (2022) https://doi.org/10.1117/12.2626111
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China
Abstract
Depth from Defocus (DFD) techniques estimate the distance/depth to each point of a target object by using a set of multifocus images. Many of the DFD techniques proposed thus far have the common disadvantage that the estimation accuracy of depth decreases for an image set captured with a real/nonideal lens compared with artificial one generated based on an ideal lens model. The accuracy degradation can be attributed to a deviation from the theoretical model of lens blur, which is quite difficult to formulate using a mathematical model. To overcome the problem, we proposes a DFD technique based a convolutional neural network (CNN) whose accuracy is enough to be applied to 3-D modeling applications. In this paper, the proposed CNN is trained with computer-generated artificial data sets to investigate the potentiality of the CNN-based DFD approach. The experimental results indicate that the proposed CNN achieves a comparable estimation accuracy for simulation data sets compared with one of state-of-the-art DFD techniques.
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Mizuki Kaneda and Toshiyuki Yoshida "CNN-based realization of Depth from Defocus technique", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217724 (30 April 2022); https://doi.org/10.1117/12.2626111
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KEYWORDS
3D modeling
Point spread functions
Data modeling
Mathematical modeling
Convolution
Cameras
Convolutional neural networks
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