Advanced Three-Dimensional Visualization System for an Integral Imaging Microscope Using a Fully Convolutional Depth Estimation Network
| Autor: | Min Young Kim, Yan-Ling Piao, Young-Tae Lim, Yu Zhao, Ki-Chul Kwon, Ki Hoon Kwon, Nam Kim, Munkh-Uchral Erdenebat |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Applied optics. Photonics
Microscope Computer science Integral imaging microscopy ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Iterative reconstruction 01 natural sciences Convolutional neural network law.invention 010309 optics law 0103 physical sciences lcsh:QC350-467 Computer vision Electrical and Electronic Engineering Image resolution Integral imaging Color image business.industry lcsh:TA1501-1820 resolution enhancement 021001 nanoscience & nanotechnology Atomic and Molecular Physics and Optics high-quality reconstruction Visualization Computer Science::Computer Vision and Pattern Recognition fully convolutional depth estimation network Noise (video) Artificial intelligence 0210 nano-technology business lcsh:Optics. Light |
| Zdroj: | IEEE Photonics Journal, Vol 12, Iss 4, Pp 1-14 (2020) |
| ISSN: | 1943-0655 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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