Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures.

Area of Science:

• Scientific Imaging
• Artificial Intelligence
• Data Synthesis

Background:

• Generative AI (genAI) offers powerful capabilities for synthesizing complex image data.
• Scientific imaging applications can benefit from novel image generation techniques.

Purpose of the Study:

• To conduct a comparative analysis of leading generative AI architectures for scientific image synthesis.
• To evaluate Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models.

Main Methods:

• Evaluation on domain-specific datasets (microCT scans, plant roots).
• Integration of quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and qualitative expert assessments.
• Analysis of foundational principles, architectural advancements, and practical trade-offs.

Main Results:

• Generative Adversarial Networks (GANs), especially StyleGAN, demonstrated high perceptual quality and structural coherence.
• Diffusion models showed high realism and semantic alignment but faced challenges in balancing visual fidelity with scientific accuracy.
• Limitations of standard quantitative metrics in assessing scientific relevance were identified, highlighting the need for expert validation.

Conclusions:

• Generative AI holds significant potential for scientific data augmentation, simulation, and hypothesis generation.
• Key challenges include model interpretability, computational expense, and robust verification protocols.
• Domain-expert validation is crucial for ensuring the scientific relevance of generated images.