Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.

翻译：去噪扩散模型代表了计算机视觉中一个新兴的研究课题，在生成建模领域展现了显著成果。扩散模型是一种深度生成模型，包含两个阶段：正向扩散阶段和反向扩散阶段。在正向扩散阶段，输入数据通过逐步添加高斯噪声而逐渐扰动；在反向阶段，模型通过学习逐步逆转扩散过程（逐步骤进行），致力于恢复原始输入数据。尽管扩散模型存在计算负担（即采样过程中因步骤较多导致速度较低），但其生成样本的质量和多样性广受认可。本综述全面梳理了应用于视觉领域的去噪扩散模型文献，涵盖该领域的理论与实际贡献。首先，我们识别并提出了三种通用扩散建模框架，分别基于去噪扩散概率模型、噪声条件得分网络和随机微分方程。我们进一步讨论了扩散模型与其他深度生成模型（包括变分自编码器、生成对抗网络、基于能量的模型、自回归模型和归一化流）之间的关系。随后，我们介绍了应用于计算机视觉的扩散模型的多视角分类体系。最后，我们阐述了扩散模型当前的局限性，并展望了未来研究中一些有意义的方向。