A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images

2025-02-28

AI Summary

This work systematically evaluates the applicability of generative AI to scientific image understanding, focusing on text-to-image and image-to-image generation tasks. We propose the first horizontal evaluation framework tailored to scientific imaging scenarios, benchmarking three dominant generative architectures—VAEs, GANs, and diffusion models—across six quantitative dimensions: fidelity, controllability, physical consistency, noise robustness, fine-grained detail accuracy, and domain adaptation efficiency. To address domain-specific requirements, we introduce novel evaluation metrics for generative quality in scientific imaging. Our analysis reveals fundamental trade-offs among key performance indicators across architectures. Furthermore, we identify concrete technical pathways toward enhancing model interpretability. Collectively, these findings provide both theoretical foundations and practical guidelines for the reliable deployment of generative AI in computational imaging, microscopy analysis, and other scientific domains.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field.

Problem

• Survey state-of-the-art text-to-image and image-to-image generation.
• Compare architectures: Variational Autoencoders, GANs, Diffusion Models.
• Discuss challenges and future research in scientific image understanding.

Innovation

• Comparative analysis of generative AI architectures
• Focus on Variational Autoencoders, GANs, Diffusion Models
• Addressing scientific image understanding challenges