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

TL;DR

This paper reviews current generative AI techniques for text-to-image and image-to-image tasks, focusing on architectures like VAEs, GANs, and Diffusion Models, and discusses their implications for scientific images.

Contribution

It provides a comparative analysis of key generative AI architectures and discusses their strengths, limitations, and future challenges in scientific image applications.

Findings

Analyzes VAEs, GANs, and Diffusion Models for scientific images.

Highlights strengths and limitations of each architecture.

Discusses open challenges and future research directions.

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.