Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network
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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9144380/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849422664186200064 |
|---|---|
| author | Ki-Chul Kwon Ki Hoon Kwon Munkh-Uchral Erdenebat Yan-Ling Piao Young-Tae Lim Yu Zhao Min Young Kim Nam Kim |
| author_facet | Ki-Chul Kwon Ki Hoon Kwon Munkh-Uchral Erdenebat Yan-Ling Piao Young-Tae Lim Yu Zhao Min Young Kim Nam Kim |
| author_sort | Ki-Chul Kwon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1eade5ae99a44e0fbf96214ff1854638 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-1eade5ae99a44e0fbf96214ff18546382025-08-20T03:30:57ZengIEEEIEEE Photonics Journal1943-06552020-01-0112411410.1109/JPHOT.2020.30103199144380Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation NetworkKi-Chul Kwon0https://orcid.org/0000-0003-0334-504XKi Hoon Kwon1https://orcid.org/0000-0003-3357-0838Munkh-Uchral Erdenebat2https://orcid.org/0000-0001-6941-8577Yan-Ling Piao3https://orcid.org/0000-0001-8809-6243Young-Tae Lim4https://orcid.org/0000-0002-3087-0619Yu Zhao5Min Young Kim6https://orcid.org/0000-0001-7263-3403Nam Kim7https://orcid.org/0000-0001-8109-2055School of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk, South KoreaSchool of Electronics Engineering, Kyungpook National University, Daegu, South KoreaSchool of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk, South KoreaSchool of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk, South KoreaSchool of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk, South KoreaCollege of Information Engineering, Yangzhou University, Yangzhou, ChinaSchool of Electronics Engineering, Kyungpook National University, Daegu, South KoreaSchool of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk, South KoreaIn 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.https://ieeexplore.ieee.org/document/9144380/Integral imaging microscopyresolution enhancementhigh-quality reconstructionfully convolutional depth estimation network |
| spellingShingle | Ki-Chul Kwon Ki Hoon Kwon Munkh-Uchral Erdenebat Yan-Ling Piao Young-Tae Lim Yu Zhao Min Young Kim Nam Kim Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network IEEE Photonics Journal Integral imaging microscopy resolution enhancement high-quality reconstruction fully convolutional depth estimation network |
| title | Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network |
| title_full | Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network |
| title_fullStr | Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network |
| title_full_unstemmed | Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network |
| title_short | Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network |
| title_sort | advanced three dimensional visualization system for an integral imaging microscope using a fully convolutional depth estimation network |
| topic | Integral imaging microscopy resolution enhancement high-quality reconstruction fully convolutional depth estimation network |
| url | https://ieeexplore.ieee.org/document/9144380/ |
| work_keys_str_mv | AT kichulkwon advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT kihoonkwon advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT munkhuchralerdenebat advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT yanlingpiao advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT youngtaelim advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT yuzhao advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT minyoungkim advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork AT namkim advancedthreedimensionalvisualizationsystemforanintegralimagingmicroscopeusingafullyconvolutionaldepthestimationnetwork |