Super precision spectral encoding for laser imaging via machine learning
Laser Imaging is an important technology nowadays, which involves using of cameras based on CCDs. However, all the colors are associated with distinct emission wavelength of laser, and CCDs not be capable of distinguishing them, rendering the RGB images captured by such cameras may not show adequate...
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2022
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sg-ntu-dr.10356-1561282023-07-04T17:49:37Z Super precision spectral encoding for laser imaging via machine learning Liu, Yunke Y. C. Chen School of Electrical and Electronic Engineering yucchen@ntu.edu.sg Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Laser Imaging is an important technology nowadays, which involves using of cameras based on CCDs. However, all the colors are associated with distinct emission wavelength of laser, and CCDs not be capable of distinguishing them, rendering the RGB images captured by such cameras may not show adequate information about the laser emission wavelength. Machine Learning is a useful tool, which enables to build a model between the colors and wavelength, provided with adequate amount of data. In our work, a neural network containing residual units is used to build the model between RGB values of laser images in small blocks and the corresponding laser emission wavelength, is trained, and is used on images containing liquid crystal droplets, to make predictions of emission wavelength in every part of these images. Master of Science (Signal Processing) 2022-04-04T07:27:06Z 2022-04-04T07:27:06Z 2021 Thesis-Master by Coursework Liu, Y. (2021). Super precision spectral encoding for laser imaging via machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156128 https://hdl.handle.net/10356/156128 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Liu, Yunke Super precision spectral encoding for laser imaging via machine learning |
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Laser Imaging is an important technology nowadays, which involves using of cameras based on CCDs. However, all the colors are associated with distinct emission wavelength of laser, and CCDs not be capable of distinguishing them, rendering the RGB images captured by such cameras may not show adequate information about the laser emission wavelength.
Machine Learning is a useful tool, which enables to build a model between the colors and wavelength, provided with adequate amount of data. In our work, a neural network containing residual units is used to build the model between RGB values of laser images in small blocks and the corresponding laser emission wavelength, is trained, and is used on images containing liquid crystal droplets, to make predictions of emission wavelength in every part of these images. |
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Y. C. Chen |
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Y. C. Chen Liu, Yunke |
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Thesis-Master by Coursework |
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Liu, Yunke |
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Liu, Yunke |
title |
Super precision spectral encoding for laser imaging via machine learning |
title_short |
Super precision spectral encoding for laser imaging via machine learning |
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Super precision spectral encoding for laser imaging via machine learning |
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Super precision spectral encoding for laser imaging via machine learning |
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Super precision spectral encoding for laser imaging via machine learning |
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super precision spectral encoding for laser imaging via machine learning |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/156128 |
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