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Realistic and Robust Image Transfer using Deep Learning

by Rhitik Prajapati, Sonal Fatangare, Shreya Nikam, Devashri Suravase, Tilak Raut
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 7
Year of Publication: 2025
Authors: Rhitik Prajapati, Sonal Fatangare, Shreya Nikam, Devashri Suravase, Tilak Raut
10.5120/ijca2025924954

Rhitik Prajapati, Sonal Fatangare, Shreya Nikam, Devashri Suravase, Tilak Raut . Realistic and Robust Image Transfer using Deep Learning. International Journal of Computer Applications. 187, 7 ( May 2025), 20-25. DOI=10.5120/ijca2025924954

@article{ 10.5120/ijca2025924954,
author = { Rhitik Prajapati, Sonal Fatangare, Shreya Nikam, Devashri Suravase, Tilak Raut },
title = { Realistic and Robust Image Transfer using Deep Learning },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 7 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number7/realistic-and-robust-image-transfer-using-deep-learning/ },
doi = { 10.5120/ijca2025924954 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-29T00:03:16.128481+05:30
%A Rhitik Prajapati
%A Sonal Fatangare
%A Shreya Nikam
%A Devashri Suravase
%A Tilak Raut
%T Realistic and Robust Image Transfer using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 7
%P 20-25
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the ever-evolving world of deep learning, creating photorealistic photo editing poses is challenging, especially when modifying features such as hairstyles in photos. The system leverages advanced generative adversarial networks (GANs) to solve problems such as misalignment, texturing, and lighting conflicts. A dedicated color adjustment module controls hair color change even under different lighting conditions, while a refinement module restores fine details for highly realistic final images. Recent solutions have shown significant improvements in both speed and accuracy. These advances are paving the way for more implementation in areas like virtual experiments, interactive tournaments, and design tools. In this survey, we examine the most advanced deep learning techniques for processing real-life images, focusing on their ability to handle complex transformations like hair editing.

References
  1. Aljohani, A., & Alharbe, N. (2022). Generating Synthetic Images for Healthcare with Novel Deep Pix2Pix GAN. Electronics, 11(21), 3470. https://doi.org/10.3390/electronics11213470
  2. Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1809.11096
  3. Chen, Q., & Koltun, V. (2017). Photographic Image Synthesis with Cascaded Refinement Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1707.09405
  4. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NETS. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1606.03657
  5. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2010.11929
  6. Dosovitskiy, A., & Brox, T. (2016). Generating Images with Perceptual Similarity Metrics based on Deep Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1602.02644
  7. Esser, P., Rombach, R., & Ommer, B. (2020). Taming Transformers for High-Resolution Image Synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2012.09841
  8. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1406.2661
  9. Huang, X., & Belongie, S. J. (2017). Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1703.06868
  10. Isola, P., Zhu, J., Zhou, T., & Efros, A. A. (2016). Image-to-Image Translation with Conditional Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1611.07004
  11. Karmakar, A., & Mishra, D. (2020). A robust pose Transformational GAN for pose guided person image synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2001.01259
  12. Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1710.10196
  13. Karras, T., Laine, S., & Aila, T. (2018). A Style-Based generator architecture for generative adversarial networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1812.04948
  14. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2019). Analyzing and improving the image quality of StyleGAN. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1912.04958
  15. Kim, J., Lee, S., & Kang, S. (2021). End-to-End differentiable learning to HDR image synthesis for multi-exposure images. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1780–1788. https://doi.org/10.1609/aaai.v35i2.16272
  16. Kingma, D. P., & Welling, M. (2013). Auto-Encoding variational Bayes. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1312.6114
  17. Li, B., Qi, X., Lukasiewicz, T., & Torr, P. H. S. (2019). Controllable Text-to-Image generation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1909.07083
  18. Li, C., & Wand, M. (2016). Combining Markov random fields and convolutional neural networks for image synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1601.04589
  19. Liu, M., & Tuzel, O. (2016). Coupled generative adversarial networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1606.07536
  20. Lu, Z., Li, Z., Cao, J., He, R., & Sun, Z. (2017). Recent progress of face image synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1706.04717
  21. Mikołajczyk, A., & Grochowski, M. (2019). Style transfer-based image synthesis as an efficient regularization technique in deep learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1905.10974
  22. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial Nets. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1411.1784
  23. Nie, D., Trullo, R., Lian, J., Wang, L., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. (2018). Medical Image Synthesis with Deep Convolutional Adversarial Networks. IEEE Transactions on Biomedical Engineering, 65(12), 2720–2730. https://doi.org/10.1109/tbme.2018.2814538
  24. Nikolaev, M., Kuznetsov, M., Vetrov, D., & Alanov, A. (2024). HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.01094
  25. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context Encoders: Feature learning by inpainting. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1604.07379
  26. Pektas, M., Gecer, B., & Ugur, A. (2022). Efficient Hair Style Transfer with Generative Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2210.12524
  27. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1511.06434
  28. Rojas-Gomez, R. A., Yeh, R. A., Do, M. N., & Nguyen, A. (2021). Inverting adversarially robust networks for image synthesis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2106.06927
  29. Sarkar, K., Golyanik, V., Liu, L., & Theobalt, C. (2021). Style and Pose Control for Image Synthesis of Humans from a Single Monocular View. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2102.11263
  30. Tsirikoglou, A., Eilertsen, G., & Unger, J. (2020a). A survey of image Synthesis Methods for Visual Machine Learning. Computer Graphics Forum, 39(6), 426–451. https://doi.org/10.1111/cgf.14047
  31. Tsirikoglou, A., Eilertsen, G., & Unger, J. (2020b). A survey of image Synthesis Methods for Visual Machine Learning. Computer Graphics Forum, 39(6), 426–451. https://doi.org/10.1111/cgf.14047
  32. Van Den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1601.06759
  33. Vondrick, C., Pirsiavash, H., & Torralba, A. (2016). Generating Videos with Scene Dynamics. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1609.02612
  34. Wang, L., Chen, W., Yang, W., Bi, F., & Yu, F. R. (2020). A State-of-the-Art Review on image synthesis with generative adversarial networks. IEEE Access, 8, 63514–63537. https://doi.org/10.1109/access.2020.2982224
  35. Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., & Catanzaro, B. (2017). High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1711.11585
  36. Wang, X., & Gupta, A. (2016). Generative Image Modeling using Style and Structure Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1603.05631
  37. Xian, W., Sangkloy, P., Agrawal, V., Raj, A., Lu, J., Fang, C., Yu, F., & Hays, J. (2017). TextureGAN: Controlling Deep Image Synthesis with Texture Patches. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1706.02823
  38. Zhan, F., Yu, Y., Wu, R., Zhang, J., Lu, S., Liu, L., Kortylewski, A., Theobalt, C., & Xing, E. (2021). Multimodal image Synthesis and Editing: the Generative AI Era. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2112.13592
  39. Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017). StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1710.10916
  40. Zhang, M., & Zheng, Y. (2018). Hair-GANs: Recovering 3D Hair Structure from a Single Image. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1811.06229
  41. Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1801.03924
  42. Zhao, J. J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1609.03126.
  43. Zhu, J., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1703.10593
Index Terms

Computer Science
Information Sciences

Keywords

Generative Adversarial Networks (GANs) Image-to-Image Translation Encoder-Based Approach StyleGAN Pose Alignment Shape and Color Alignment Image Synthesis

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