用图像到图像扩散模型提升脑部MRI的细节与真实感
RealDeal: Enhancing Realism and Details in Brain Image Generation via Image-to-Image Diffusion Models
- 将生成图像通过扩散模型重构，增强边缘、纹理和解剖细节
- 在FID和LPIPS上优于基线模型，噪声分布更接近真实扫描数据
- 适合需要高保真医学图像的研究者或临床模拟场景
我们提出一种图像到图像扩散模型，用于提升生成脑部MRI图像的真实感与细节。尽管潜空间扩散模型在脑部MRI生成中表现优异，但因潜在空间压缩导致图像过于平滑，缺乏真实图像中的精细解剖结构和扫描噪声。本工作将真实感增强与细节添加建模为图像到图像扩散过程，对潜空间生成结果进行质量优化。采用FID和LPIPS等常用指标评估图像真实性，并引入新指标量化图像噪声分布、锐度与纹理特征。实验表明，RealDeal生成图像在多个维度更贴近真实扫描数据。
原文摘要 · Abstract (English)
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.
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