Abstract
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.
Abstract (translated)
扩散模型在生成建模方面取得了显著的进步,特别是在提高图像质量以符合人类偏好方面。最近,这些模型还应用于低级计算机视觉中的 photo-realistic 图像修复(IR)任务,例如图像去噪、去雾等。在本文综述论文中,我们介绍了扩散模型的关键构建,并调查了使用扩散模型解决一般 IR 任务的当代技术。此外,我们指出了现有基于扩散的 IR 框架的主要挑战和局限性,并为未来的工作提供了潜在方向。
URL
https://arxiv.org/abs/2409.10353