We propose image-to-image diffusion models that are designed to enhance the realism and details of generated brain images by introducing sharp edges, fine textures, subtle anatomical features, and imaging noise. Generative models have been widely adopted in the biomedical domain, especially in image generation applications. Latent diffusion models achieve state-of-the-art results in generating brain MRIs. However, due to latent compression, generated images from these models are overly smooth, lacking fine anatomical structures and scan acquisition noise that are typically seen in real images. This work formulates the realism enhancing and detail adding process as image-to-image diffusion models, which refines the quality of LDM-generated images. We employ commonly used metrics like FID and LPIPS for image realism assessment. Furthermore, we introduce new metrics to demonstrate the realism of images generated by RealDeal in terms of image noise distribution, sharpness, and texture.
@article{arxiv.2507.18830,
title = {RealDeal: Enhancing Realism and Details in Brain Image Generation via Image-to-Image Diffusion Models},
author = {Shen Zhu and Yinzhu Jin and Tyler Spears and Ifrah Zawar and P. Thomas Fletcher},
journal= {arXiv preprint arXiv:2507.18830},
year = {2025}
}