Mr. Mizuki Kaneda Profile

Mizuki Kaneda

at Univ of Fukui

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Proceedings Article | 1 May 2022 Paper

CNN-based realization of Depth from Defocus technique

Mizuki Kaneda, Toshiyuki Yoshida

Proceedings Volume 12177, 1217724 (2022) https://doi.org/10.1117/12.2626111

KEYWORDS: 3D modeling, Point spread functions, Data modeling, Mathematical modeling, Convolution, Convolutional neural networks, Cameras

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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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