fire-GAN: Flame Image Generation Algorithm Based on Generative Adversarial Network

Table 1. Roundness of flames and disturbances
View table
Table 1. Roundness of flames and disturbances
Parameter Flame Flashlight Car lights Sunlight through windows
C 0.279 0.714 0.666 0.585

Table 2. Quantitative evaluation of flame image generated by two parts of datasets
View table
Table 2. Quantitative evaluation of flame image generated by two parts of datasets
Dataset FID IS
Without flame segmentation 80.09 2.39
With flame segmentation 59.23 2.81

Table 3. Comparison of average values of flame roundness in images generated under different conditions of roundness loss function
View table
Table 3. Comparison of average values of flame roundness in images generated under different conditions of roundness loss function
Parameter Without loss R_{d_loss} R_{g_loss} R_{d_loss}+R_{g_loss} Real image
C 0.434 0.379 0.330 0.319 0.279

Table 4. Comparison of R, G, and B mean values of images generated by fire-GAN and MixNMatch
View table
Table 4. Comparison of R, G, and B mean values of images generated by fire-GAN and MixNMatch
Network R G B
Target image 233.6397 217.8905 201.2066
fire-GAN 228.3991 213.4572 194.0862
MixNMatch 204.1542 203.5226 171.0034

Table 5. Quantitative evaluation of different networks
View table
Table 5. Quantitative evaluation of different networks
Parameter GAN SAGAN MixNMatch content-aware-GAN styleGAN2 fire-GAN
FID 129.02 125.35 140.34 68.85 60.52 59.23
IS 2.06 2.00 1.91 2.76 2.79 2.81

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Kui Qin, Xinguo Hou, Feng Zhou, Zhengjun Yan, Leping Bu. fire-GAN: Flame Image Generation Algorithm Based on Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1210008

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Paper Information

Category: Image Processing

Received: Mar. 14, 2022

Accepted: Jun. 13, 2022

Published Online: May. 23, 2023

The Author Email: Hou Xinguo (hxinguo2008@126.com)

DOI:10.3788/LOP220989

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Roundness of flames and disturbances

Table 1. Roundness of flames and disturbances

Table 2. Quantitative evaluation of flame image generated by two parts of datasets

Table 2. Quantitative evaluation of flame image generated by two parts of datasets

Table 3. Comparison of average values of flame roundness in images generated under different conditions of roundness loss function

Table 3. Comparison of average values of flame roundness in images generated under different conditions of roundness loss function

Table 4. Comparison of R, G, and B mean values of images generated by fire-GAN and MixNMatch

Table 4. Comparison of R, G, and B mean values of images generated by fire-GAN and MixNMatch

Table 5. Quantitative evaluation of different networks

Table 5. Quantitative evaluation of different networks