AI-Driven Image Generation: Algorithms, Architectures, and Applications

Dear Colleagues,

Over the past decade, significant progress has been made in the development of generative models driven by artificial intelligence, enabling machines to produce photorealistic and creative images that were previously thought to be the domain of human artists. Techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and more recently, Diffusion Models, have revolutionized the landscape of image synthesis, allowing for the generation of high-quality images from diverse input data, such as text descriptions, sketches, or even random noise. The ability to automatically create photorealistic and contextually appropriate visual content opens up new possibilities for creativity, enhances computational efficiency, and provides powerful tools for industries that rely heavily on image processing and content generation. By advancing these areas, we aim to provide an in-depth examination of the latest algorithms, architectures, and applications in this domain, offering insights into both the theoretical foundations and real-world impact of AI-powered image generation.

We invite submissions that focus on novel algorithms, architectures, and frameworks for image generation, with an emphasis on their underlying mathematical and computational principles. Contributions that explore the real-world applications of AI-driven image generation across diverse domains—such as computer vision, art and design, entertainment, and healthcare—are equally welcome. Of special interest are studies that address challenges of scalability, efficiency, controllability, interpretability, and ethical deployment of generative models. We also encourage submissions that propose evaluation methodologies, benchmark datasets, and metrics for assessing the quality, realism, and trustworthiness of generated images.

The topics covered include, but are not restricted to, the following:

- Novel algorithms, models, and architectures for AI-driven image generation;

- Generative adversarial networks (GANs), diffusion models, VAEs, and hybrid approaches;

- Multimodal frameworks integrating text, audio, video, or 3D data for image synthesis;

- Controllability, interpretability, and explainability in generative models;

- Scalable and efficient training strategies for large-scale image generation;

- Benchmark datasets, evaluation methodologies, and quality assessment metrics;

- Applications in computer vision, digital art and design, entertainment, healthcare, and education;

- AI-assisted creativity, human–AI collaboration, and interactive design tools;

- Ethical, legal, and societal implications of AI-generated imagery, including authenticity and bias;

- Emerging trends and future directions in AI-driven image generation.

We look forward to receiving your contributions.

Dr. Yi Zhang
Prof. Dr. Damon M. Chandler
Prof. Dr. Mikolaj Leszczuk
Prof. Dr. Mylène C. Q. Farias
Guest Editors

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• AI-driven image generation
• generative adversarial networks
• diffusion models
• controllability and interpretability
• evaluation metrics and benchmarks
• human–AI collaboration
• applications in healthcare, art and design
• ethical and responsible AI