Optoelectronics Letters, Volume. 21, Issue 9, 555(2025)
Data augmentation method for light guide plate based on improved CycleGAN
[1] [1] JAVAID M, HALEEM A, SINGH R P, et al. Exploring impact and features of machine vision for progressive industry 4.0 culture[J]. Sensors international, 2022, 3: 100-132.
[2] [2] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. (2015-11-19) [2024-01-23]. http://arxiv.org/abs/1511.06434?context=cs.LG.htm.
[3] [3] LIN Z P, ZENG L B, WU Q S. Cervical cell image data enhancement based on generative adversarial network[J]. Science technology and engineering, 2020, 20(28): 11672-11677. (in Chinese)
[4] [4] WANG J N, SU J, YANG K. Insulator image generation method based on Cycle-GAN[J]. Guangdong electric power, 2020, 33(01): 100-108. (in Chinese)
[5] [5] ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2223-2232.
[6] [6] SONG Z W, YAO H, TIAN D, et al. Improved Cycle-GAN for super-resolution of engineering drawings[J]. Measurement science and technology, 2023, 34(7).
[7] [7] CHOI Y, CHOI M, KIM M, et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 8789-8797.
[8] [8] THAMBAWITA V, SALEHI P, SHESHKAL S A, et al. SinGAN-Seg: synthetic training data generation for medical image segmentation[J]. PloS one, 2022, 17(5): e0267976.
[9] [9] SHEN Y, HUANG R, HUANG W. GD-StarGAN: multi-domain image-to-image translation in garment design[J]. PloS one, 2020, 15(4): e0231719.
[10] [10] LUCEY P, COHN J F, KANADE T, et al. The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2010: 94-101.
[11] [11] SUN X, DING X L. Facial expression data enhancement method based on generative adversarial network[J]. Computer engineering and applications, 2020, 56(04): 115-121. (in Chinese)
[12] [12] LIU K, WEN X, HUANG M, et al. Solar cell defect enhancement method based on generative adversarial network[J]. Journal of Zhejiang University (engineering science), 2020, 54(4): 684-693.
[13] [13] YANG Z Z, SHAO J, YANG Y P. An improved CycleGAN for data augmentation in person re-identification[J]. Big data research, 2023, 34(28).
[14] [14] ZHU L, WANG X, KE Z, et al. BiFormer: vision transformer with bi-level routing attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-22, 2023, Vancouver, Canada. New York: IEEE, 2023: 10323-10333.
[15] [15] GLENN J. Ultralytics/yolov5 v6.0[EB/OL]. (2021-10-21) [2024-01-23]. https://github.com/ultralytics/yolov5/releases/tag/v6.0.
Get Citation
Copy Citation Text
GONG Yefei, YAN Chao, XIAO Ming, LU Mingli, GAO Hua. Data augmentation method for light guide plate based on improved CycleGAN[J]. Optoelectronics Letters, 2025, 21(9): 555
Category: Image and Information processing
Received: Apr. 13, 2024
Accepted: Sep. 15, 2025
Published Online: Sep. 15, 2025
The Author Email: