100 kHz CH2O imaging realized by lower speed planar laser-induced fluorescence and deep learning
(2021) In Optics Express 29(19). p.30857-30877- Abstract
This paper reports an approach to interpolate planar laser-induced fluorescence (PLIF) images of CH2O between consecutive experimental data by means of computational imaging realized with convolutional neural network (CNN). Such a deep learning based method can achieve higher temporal resolution for 2D visualization of intermediate species in combustion based on high-speed experimental images. The capability of the model was tested for generating 100 kHz PLIF images by interpolating single and multiple PLIF frames into the sequences of experimental images of lower frequencies (50, 33, 25 and 20 kHz). Results show that the prediction indices, including intersection over union (IoU), peak signal to noise ratio (PSNR), structural... (More)
This paper reports an approach to interpolate planar laser-induced fluorescence (PLIF) images of CH2O between consecutive experimental data by means of computational imaging realized with convolutional neural network (CNN). Such a deep learning based method can achieve higher temporal resolution for 2D visualization of intermediate species in combustion based on high-speed experimental images. The capability of the model was tested for generating 100 kHz PLIF images by interpolating single and multiple PLIF frames into the sequences of experimental images of lower frequencies (50, 33, 25 and 20 kHz). Results show that the prediction indices, including intersection over union (IoU), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and time averaged correlation coefficient at various axial positions could achieve acceptable accuracy. This work sheds light on the utilization of CNN-based models to achieve optical flow computation and image sequence interpolation, also providing an efficient off-line model as an alternative pathway to overcome the experimental challenges of the state-of-the-art ultra-high speed PLIF techniques, e.g., to further increase repetition rate and save data transfer time.
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- author
- Zhang, Wei ; Dong, Xue ; Sun, Zhiwei ; Zhou, Bo ; Wang, Zhenkan LU and Richter, Mattias LU
- organization
- publishing date
- 2021-09-13
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Optics Express
- volume
- 29
- issue
- 19
- pages
- 21 pages
- publisher
- Optical Society of America
- external identifiers
-
- scopus:85114823066
- pmid:34614804
- ISSN
- 1094-4087
- DOI
- 10.1364/OE.433785
- language
- English
- LU publication?
- yes
- id
- d1ca6474-e712-46b9-b3f7-d6463194e12f
- date added to LUP
- 2021-10-08 13:33:13
- date last changed
- 2024-05-04 13:37:03
@article{d1ca6474-e712-46b9-b3f7-d6463194e12f,
abstract = {{<p>This paper reports an approach to interpolate planar laser-induced fluorescence (PLIF) images of CH2O between consecutive experimental data by means of computational imaging realized with convolutional neural network (CNN). Such a deep learning based method can achieve higher temporal resolution for 2D visualization of intermediate species in combustion based on high-speed experimental images. The capability of the model was tested for generating 100 kHz PLIF images by interpolating single and multiple PLIF frames into the sequences of experimental images of lower frequencies (50, 33, 25 and 20 kHz). Results show that the prediction indices, including intersection over union (IoU), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and time averaged correlation coefficient at various axial positions could achieve acceptable accuracy. This work sheds light on the utilization of CNN-based models to achieve optical flow computation and image sequence interpolation, also providing an efficient off-line model as an alternative pathway to overcome the experimental challenges of the state-of-the-art ultra-high speed PLIF techniques, e.g., to further increase repetition rate and save data transfer time. </p>}},
author = {{Zhang, Wei and Dong, Xue and Sun, Zhiwei and Zhou, Bo and Wang, Zhenkan and Richter, Mattias}},
issn = {{1094-4087}},
language = {{eng}},
month = {{09}},
number = {{19}},
pages = {{30857--30877}},
publisher = {{Optical Society of America}},
series = {{Optics Express}},
title = {{100 kHz CH2O imaging realized by lower speed planar laser-induced fluorescence and deep learning}},
url = {{http://dx.doi.org/10.1364/OE.433785}},
doi = {{10.1364/OE.433785}},
volume = {{29}},
year = {{2021}},
}