1 INTRODUCTION

2 A WALK-THROUGH OF DIFFUSION MODELS  
2.1 Denoising Diffusion Probabilistic Models  
2.2 Noise Conditioned Score Networks  
2.3 Stochastic Differential Equations  
2.4 Comparisons With Other Deep Generative Models  

3 DIFFUSION MODELS FOR NATURAL IMAGE PROCESSING IN LOW-LEVEL VISION  
3.1 DM-based methods with different training manners  
3.2 DM-based methods with different application goals  

4 EXTENDED DIFFUSION MODELS  
4.1 Diffusion models for medical image processing  
4.2 Diffusion models for remote sensing data  
4.3 Diffusion models for video processing  

5 EXPERIMENTS  
5.1 Datasets  
5.2 Evaluation metrics  
5.3 Experimental results  

6 FUTURE DIRECTIONS  
6.1 Mitigating the limitations of DMs  
6.2 Amalgamating the strengths of DMs with the traits of low-level vision  
6.3 Tackling the inherent challenges of low-level vision  

7 CONCLUSIONS