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Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network
Year of publication
2020
Author
Ki-Chul Kwon, Ki Hoon Kwon, Munkh-Uchral Erdenebat, Yan-Ling Piao, Young-Tae Lim , Yu Zhao, Min Young Kim , Nam Kim
Journal
IEEE Photonics Journal
volume
1943-0655
Issue
4
Page
3900714
File
첨부 Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network.pdf (6.8M) 0회 다운로드 DATE : 2020-09-11 11:39:36

In this paper, we propose an advanced three-dimensional visualization method for an integral imaging microscope system to simultaneously improve the resolution and quality of the reconstructed image. The main advance of the proposed method is that it generates a high-quality three-dimensional model without limitation of resolution by combining the high-resolution two-dimensional color image with depth data obtained through a fully convolutional neural network. First, the high-resolution two-dimensional image and an elemental image array for a specimen are captured, and the orthographic-view image is reconstructed from the elemental image array. Then, via a convolutional neural network-based depth estimation after the brightness of input images are uniformed, a more accurate and improved depth image is generated; and the noise of result depth image is filtered. Subsequently, the estimated depth data is combined with the high-resolution two-dimensional image and transformed into a high-quality three-dimensional model. In the experiment, it was confirmed that the displayed high-quality three-dimensional model could be visualized very similarly to the original image.



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