综述扩散模型在图像修复中的应用与挑战
Taming Diffusion Models for Image Restoration: A Review
- 梳理扩散模型核心结构与图像修复技术
- 总结去噪、去模糊等任务的最新方法
- 指出现有框架局限并提出未来方向
扩散模型在生成建模方面取得显著进展，尤其在提升图像质量以符合人类偏好方面表现突出。近期，这些模型也被应用于低层计算机视觉任务，实现照片级真实的图像修复（IR），包括图像去噪、去模糊、去雾等。本文综述了扩散模型的关键构建，并系统调研了当前利用扩散模型解决通用图像修复任务的技术。此外，文章指出现有基于扩散模型的图像修复框架的主要挑战与局限，并为未来研究提供潜在方向。
原文摘要 · Abstract (English)
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image restoration (IR) in tasks such as image denoising, deblurring, dehazing, etc. In this review paper, we introduce key constructions in diffusion models and survey contemporary techniques that make use of diffusion models in solving general IR tasks. Furthermore, we point out the main challenges and limitations of existing diffusion-based IR frameworks and provide potential directions for future work.
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