International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Generative Visual Content & Editing Suite |
|---|---|
| ABSTRACT | Generative Visual Content and Editing Suite is an advanced system that uses Artificial Intelligence (AI) and machine learning techniques to automatically create, modify, and enhance visual content such as images, graphics, and designs. The system is powered by generative models like Generative Adversarial Networks (GANs) and diffusion models, which can generate high-quality and realistic visuals based on user input such as text prompts, sketches, or reference images. This suite provides a wide range of features including image generation, background removal, object replacement, color correction, style transfer, image enhancement, and automated design suggestions. It reduces the need for manual editing skills and allows users such as designers, marketers, students, and content creators to produce professional-quality visuals efficiently. The platform integrates user-friendly tools with intelligent automation, enabling real-time editing and customization. By combining creativity with AI-driven automation, the Generative Visual Content and Editing Suite improves productivity, enhances creativity, and makes visual content creation accessible to everyone. |
| AUTHOR | MAYUR THERE, TANVI KAPGATE, SUHANI KOTAMBKAR, ACHAL CHAMATE, OMKUMAR MOURY, PROF. DR.VANITA BURADKAR Student, Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering, Research and Technology, Chandrapur, Maharashtra, India Assistant Prof., Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering, Research and Technology, Chandrapur, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403027 |
| pdf/27_Generative Visual Content & Editing Suite.pdf | |
| KEYWORDS | |
| References | [1] I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., “Generative Adversarial Nets,” Advances in Neural Information Processing Systems (NeurIPS), 2014. [2] A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” International Conference on Learning Representations (ICLR), 2016. [3] T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [4] J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” Neural Information Processing Systems (NeurIPS), 2020. [5] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” CVPR, 2022. [6] A. Radford, J. W. Kim, C. Hallacy, et al., “Learning Transferable Visual Models From Natural Language Supervision (CLIP),” International Conference on Machine Learning (ICML), 2021. [7] L. Gatys, A. Ecker, and M. Bethge, “Image Style Transfer Using Convolutional Neural Networks,” CVPR, 2016. 8] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NeurIPS, 2012. [9] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018. [10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. |
