AI-ENHANCED DIGITAL ILLUSTRATION METHODS IMPROVING PRECISION AND EFFICIENCY FOR VISUAL DESIGNERS

Raenu Kolandaisamy; Tapasmini Sahoo; Sukhada Shashank Aloni; Vijay Itnal; Gayathri B; R. Salini; Uma Maheswari G

doi:10.29121/shodhkosh.v7.i4s.2026.7505

Authors

Raenu Kolandaisamy Lecturer, Institute of Computer Science and Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, Malaysia
Dr. Tapasmini Sahoo Associate Professor, Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
Dr. Sukhada Shashank Aloni Assistant Professor, Department of Computer Engineering, A. P. Shah Institute of Technology, Thane (W), Mumbai University, India
Vijay Itnal Assistant Professor, Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
Gayathri B Assistant Professor, Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India
Dr. R. Salini Associate Professor, Department of Computer Science and Engineering, Panimalar Engineering College, Tamil Nadu, India
Uma Maheswari G Assistant Professor, Department of Mathematics, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7505

Keywords:

AI-Assisted Illustration, Deep Neural Networks, Generative Adversarial Networks, Diffusion Models, Digital Art Automation, Visual Design Efficiency

Abstract [English]

Artificial intelligence (AI) has significantly transformed the industry of digital illustration since it has enhanced accuracy, efficiency and freedom of creativity in the hands of visual designers. The advanced AI-based approaches that will be discussed in this paper include deep neural networks (DNNs), Generative Adversarial Networks (GANs), and diffusion models that will be used to improve the image generation and refinement process. The specified framework is a combination of data-driven learning and artistic mechanisms to ensure that it is possible to synthesize style automatically, make images look in the high-resolution, and engage in intelligent image enhancement. A systematic process is developed involving the data set preparation using a number of artistic methods, the best methodology in training the models, and implementing the AI-based illustration chain. Empirical analysis reveals that the accuracy of rendering and consistency of style and speed of production have continued to increase in comparison with the conventional processes of illustrations. The system also reduces the human error and reduces the degree of manual control, and maintains the creative control. However such problems as excessive computing requirements, reliance on data and potential bias of the outputs generated are violently discussed. The results indicate the possibility of AI-enhanced illustration systems to transform the current design process in the following ways: scalable, efficient, and quality visual production.

References

Ajani, S. N., Saoji, S., Maindargi, S. C., Rao, P. H., Patil, R. V., and Khurana, D. S. (2025). Mapping Pathways for Inclusive Digital Payment Ecosystems: Integrating NGOs, Micro-Insurance Startups, and Community Groups. Enterprise Development and Microfinance, 35(1), 61–81. https://doi.org/10.3362/1755-1986.25-00004

Barath, C.-V., Logeswaran, S., Nelson, A., Devaprasanth, M., and Radhika, P. (2023). AI in Art Restoration: A Comprehensive Review of Techniques, Case Studies, Challenges, and Future Directions. International Research Journal of Modern Engineering and Technology Science, 5, 16–21.

Baudoux, G. (2024). The Benefits and Challenges of AI Image Generators for Architectural Ideation: Study of an alternative Human–Machine Co-Creation Exchange Based on Sketch Recognition. International Journal of Architectural Computing, 22, 201–215. https://doi.org/10.1177/14780771241253438

Chang, L. (2021). Review and Prospect of Temperature and Humidity Monitoring for Cultural Property Conservation Environments. Journal of Cultural Heritage Conservation, 55, 47–55.

Cheng, Y., Zhang, Z., Yang, M., Nie, H., Li, C., Wu, X., and Shao, J. (2024). Graphic Design with Large Multimodal Model. arXiv. https://arxiv.org/abs/2404.14368

Donnici, G., Frizziero, L., Liverani, A., Buscaroli, G., Raimondo, L., Saponaro, E., and Venditti, G. (2020). A New Car Concept Developed with Stylistic Design Engineering (SDE). Inventions, 5(3), 30. https://doi.org/10.3390/inventions5030030

Huang, D., Guo, J., Sun, S., Tian, H., Lin, J., Hu, Z., Lin, C.-Y., Lou, J.-G., and Zhang, D. (2023). A Survey for Graphic Design Intelligence. arXiv.

Katya, E., and Rahman, S. (2025). Applications of Natural Language Processing in Social Media Sentiment Analysis. International Journal of Research in Advanced Engineering and Technology, 13(1), 11–15.

Li, P., Li, B., and Li, Z. (2024). Sketch-to-Architecture: Generative AI-Aided Architectural Design. arXiv.

Lin, J., Huang, D., Zhao, T., Zhan, D., and Lin, C.-Y. (2024). DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models. arXiv.

Liu, H., Li, C., Wu, Q., and Lee, Y. J. (2023). Visual Instruction Tuning. In Advances in Neural Information Processing Systems (NeurIPS 2023, Vol. 36, pp. 34892–34916). https://doi.org/10.52202/075280-1516

Sreenivasan, A., and Suresh, M. (2024). Design Thinking and Artificial Intelligence: A Systematic Literature Review Exploring Synergies. International Journal of Innovation Studies, 8, 297–312. https://doi.org/10.1016/j.ijis.2024.05.001

Tang, Y., Ciancia, M., Wang, Z., and Gao, Z. (2024). What’s Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design. arXiv.

Tang, Y., Ciancia, M., Wang, Z., and Gao, Z. (2024a). What’s Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design. arXiv.

Tang, Y., Ciancia, M., Wang, Z., and Gao, Z. (2024b). Vision-Language Models for Vision Tasks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 5625–5644. https://doi.org/10.1109/TPAMI.2024.3369699

Xiao, S., Wang, Y., Zhou, J., Yuan, H., Xing, X., Yan, R., Li, C., Wang, S., Huang, T., and Liu, Z. (2025). Omnigen: Unified Image Generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52734.2025.01241

Zhou, Y., and Park, H. J. (2023). An AI-Augmented Multimodal Application for Sketching Out Conceptual Design. International Journal of Architectural Computing, 21, 565–580. https://doi.org/10.1177/14780771221147605