用扩散模型提升显微图像分辨率，手把手教代码实现
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
- 从零构建去噪扩散概率模型，专用于显微图像超分辨率
- 提供理论推导与PyTorch完整代码，可直接运行复现
- 适合生物图像处理、医学影像领域研究者快速上手
扩散模型已成为神经网络生成建模中的主流技术，在文本到图像转换和超分辨率等任务中表现突出。本文提供一份全面教程，指导读者从零开始构建去噪扩散概率模型（DDPM），重点应用于将低分辨率显微图像转换为高分辨率版本。内容涵盖理论背景、数学推导、基于PyTorch的详细Python实现，以及提升模型性能的实用技巧。
原文摘要 · Abstract (English)
Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models (DDPMs) from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions. We provide the theoretical background, mathematical derivations, and a detailed Python code implementation using PyTorch, along with techniques to enhance model performance.
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