  Deep generative models have garnered significant attention in low-level
vision tasks due to their generative capabilities. Among them, diffusion
model-based solutions, characterized by a forward diffusion process and a
reverse denoising process, have emerged as widely acclaimed for their ability
to produce samples of superior quality and diversity. This ensures the
generation of visually compelling results with intricate texture information.
Despite their remarkable success, a noticeable gap exists in a comprehensive
survey that amalgamates these pioneering diffusion model-based works and
organizes the corresponding threads. This paper proposes the comprehensive
review of diffusion model-based techniques. We present three generic diffusion
modeling frameworks and explore their correlations with other deep generative
models, establishing the theoretical foundation. Following this, we introduce a
multi-perspective categorization of diffusion models, considering both the
underlying framework and the target task. Additionally, we summarize extended
diffusion models applied in other tasks, including medical, remote sensing, and
video scenarios. Moreover, we provide an overview of commonly used benchmarks
and evaluation metrics. We conduct a thorough evaluation, encompassing both
performance and efficiency, of diffusion model-based techniques in three
prominent tasks. Finally, we elucidate the limitations of current diffusion
models and propose seven intriguing directions for future research. This
comprehensive examination aims to facilitate a profound understanding of the
landscape surrounding denoising diffusion models in the context of low-level
vision tasks. A curated list of diffusion model-based techniques in over 20
low-level vision tasks can be found at
https://github.com/ChunmingHe/awesome-diffusion-models-in-low-level-vision.
