Automatic High-Resolution Mustache Pattern Synthesis From Denim Fabric

Abstract

Obtaining the mustache patterns on denim fabrics takes about 2-3 hours manually by an expert. Along with the temporal cost, errors occur due to person-based production. To solve this problem, Pix2PixHD architecture, one of the current Generative adversarial networks, is used in this study. For the use of this deep learning-based architecture, Denim2BıyıkHD dataset consisting of 589 Denim fabric-Mustache Pattern image pairs in 2048x1024 dimensions was prepared. For the architecture to give the most appropriate results, its improved version has been proposed. With the improved method after the training process, approximately 92% of the image quality was achieved, while the temporal production process cost was reduced to less than 1 second. With this study, a software system was developed that enables the automatic, high quality, fast and objective production of mustache patterns on denim fabrics.

Keywords

• Denim fabric
• Mustache pattern
• Image synthesis
• Generative adversarial networks

Denim Kumaşından Otomatik Yüksek Çözünürlüklü Bıyık Desen Sentezi

Abstract

Denim kumaşlarındaki bıyık desenlerinin elde edilmesi uzman bir kişi tarafından manuel olarak yaklaşık 2-3 saat sürmektedir. Zamansal maliyetle birlikte kişi bazlı üretimden kaynaklı hatalar meydana gelmektedir. Bu problemin çözümü için bu çalışmada güncel Çekişmeli üretici ağlardan biri olan Pix2PixHD mimarisi kullanılmıştır. Derin öğrenme tabanlı bu mimarinin kullanımı için 2048x1024 ebatlarındaki 589 adet Denim kumaşı-Bıyık Deseni görüntü çiftinden oluşan Denim2BıyıkHD veri kümesi hazırlanmıştır. Mimarinin en uygun sonuçları verebilmesi için üzerinde iyileştirmeler yapılarak geliştirilmiş versiyonu önerilmiştir. Eğitim işleminden sonra geliştirilmiş yöntemle birlikte görüntü kalitesinde yaklaşık 92% oranında başarım sağlanırken, zamansal üretim işlem maliyeti 1 saniyenin altına düşürülmüştür. Bu çalışmayla birlikte Denim kumaşlarındaki bıyık desenlerinin otomatik, yüksek kalitede, hızlı ve nesnel bir şekilde üretimini sağlayan yazılımsal bir sistem geliştirilmiştir.

Keywords

• Denim kumaş
• Bıyık deseni
• Görüntü Sentezi
• Çekişmeli üretici ağlar

References

1. [1] Zou, X., Wong, W. K., & Mo, D. (2018). Fashion Meets AI technology. In International Conference on Artificial Intelligence on Textile and Apparel (pp. 255-267). Springer, Cham.
2. [2] Jucienė, M., Urbelis, V., Juchnevičienė, Ž., & Čepukonė, L. (2014). The Effect of Laser Technological Parameters on The Color and Structure of Denim Fabric. Textile Research Journal, 84(6), 662-670.
3. [3] Zhong, T., Dhandapani, R., Liang, D., Wang, J., Wolcott, M. P., Van Fossen, D., & Liu, H. (2020). Nanocellulose from Recycled Indigo-dyed Denim Fabric and Its Application in Composite Films. Carbohydrate Polymers, 240, 116283.
4. [4] Golden Laser. (2022). Jeans Laser Engraving Machine. Retrieved from: https://www.goldenlaser.cc/jeans-laser-engraving-machine.html, erişim tarihi: 10 Ağustos 2022.
5. [5] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in neural information processing systems, 27.
6. [6] Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
7. [7] Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2016). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).
8. [8] Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134).

9. [9] Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2223-2232).
10. [10] Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196.
11. [11] Karras, T., Laine, S., & Aila, T. (2018). A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948.
12. [12] Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018). Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). PMLR.
13. [13] Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096.
14. [14] Wang, T. C., Liu, M. Y., Zhu, J. Y., Tao, A., Kautz, J., & Catanzaro, B. (2018). High-resolution image synthesis and semantic manipulation with conditional gans. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8798-8807).
15. [15] Park, T., Liu, M. Y., Wang, T. C., & Zhu, J. Y. (2019). Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2337-2346).
16. [16] Li, Y., Singh, K. K., Ojha, U., & Lee, Y. J. (2020). Mixnmatch: Multifactor disentanglement and encoding for conditional image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8039-8048).
17. [17] Wang, X., Li, Y., Zhang, H., & Shan, Y. (2021). Towards Real-World Blind Face Restoration with Generative Facial Prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9168-9178).
18. [18] Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., & Aila, T. (2021). Alias-free generative adversarial networks. arXiv preprint arXiv:2106.12423.
19. [19] Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., ... & Lempitsky, V. (2021). Resolution-robust Large Mask Inpainting with Fourier Convolutions. arXiv preprint arXiv:2109.07161.
20. [20]Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
21. [21] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
22. [22] Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., ... & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6881-6890).
23. [23]Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
24. [24]Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., ... & Xu, D. (2022). Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 574-584).
25. [25]Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., & Li, J. (2021, September). Transbts: Multimodal brain tumor segmentation using transformer. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 109-119). Springer, Cham.
26. [26]Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H., & Xu, D. (2022). Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. arXiv preprint arXiv:2201.01266.
27. [27] Baykan Denim A.Ş. (2022). Baykan Denim A.Ş. firmasının resmi web site adresi. Retrieved from: https://www.baykandenim.com/home-page/, erişim tarihi: 11 Ağustos 2022.
28. [28]Şahin, E., & Talu, M. F. (2022) Automatic Mustache Pattern Production on Denim Fabric with Generative Adversarial Networks. Computer Science, 7(1), 1-9.
29. [29]Şahin, E., & Talu, M. F. (2021). Bıyık Deseni Üretiminde Çekişmeli Üretici Ağların Performans Karşılaştırması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(4), 1575-1589.
30. [30]Nilsson, J., & Akenine-Möller, T. (2020). Understanding ssim. arXiv preprint arXiv:2006.13846.
31. [31] Mihelich M., Dognin C., Shu Y., Blot M. (2020). A Characterization of Mean Squared Error for Estimator with Bagging. ArXiv, abs/1908.02718.
32. [32]Fardo F A., Conforto V H., Oliveira F C., Rodrigues P. (2016). A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms. ArXiv, abs/1605.07116.