TY - JOUR
T1 - Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network
AU - Kwon, Ki Chul
AU - Kwon, Ki Hoon
AU - Erdenebat, Munkh Uchral
AU - Piao, Yan Ling
AU - Lim, Young Tae
AU - Zhao, Yu
AU - Kim, Min Young
AU - Kim, Nam
N1 - Publisher Copyright:
© 2009-2012 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - fully convolutional depth estimation network
KW - high-quality reconstruction
KW - Integral imaging microscopy
KW - resolution enhancement
UR - http://www.scopus.com/inward/record.url?scp=85090131732&partnerID=8YFLogxK
U2 - 10.1109/JPHOT.2020.3010319
DO - 10.1109/JPHOT.2020.3010319
M3 - Article
AN - SCOPUS:85090131732
SN - 1943-0655
VL - 12
JO - IEEE Photonics Journal
JF - IEEE Photonics Journal
IS - 4
M1 - 9144380
ER -