Summary of Diffusion Models in Low-level Vision: a Survey, by Chunming He et al.
Diffusion Models in Low-Level Vision: A Survey
by Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, Xiu Li
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of diffusion model-based techniques in low-level vision tasks. It discusses three generic diffusion modeling frameworks and their correlations with other deep generative models, establishing the theoretical foundation. The authors also introduce a multi-perspective categorization of diffusion models based on both the underlying framework and target task. They summarize extended diffusion models applied in medical, remote sensing, and video scenarios, and provide an overview of commonly used benchmarks and evaluation metrics. The paper conducts a thorough evaluation of diffusion model-based techniques in three prominent tasks, including performance and efficiency assessments. Finally, it elucidates the limitations of current diffusion models and proposes seven intriguing directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews the development of diffusion model-based solutions for low-level vision tasks. These models are good at generating high-quality images with detailed textures. The authors look at three main frameworks for these models and how they relate to other types of deep generative models. They also group similar models together based on what they’re used for and how they work. The paper covers how diffusion models have been applied in different areas, such as medicine and video analysis, and explains the metrics used to measure their performance. It concludes by pointing out the limitations of current models and suggesting ways to improve them. |
Keywords
» Artificial intelligence » Diffusion model
