# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 6 Conclusions  
In conclusion, this study presents a novel approach to low-light image enhancement using a patch-wise diffusion model with stable parameter optimization and uncertainty guidance. By employing random patch selection and patch-wise optimization, the proposed method effectively enforces a stable optimization path in the diffusion model, leading to improved structural preservation and perceptual quality in enhanced images. The uncertainty network further contributes by estimating and integrating uncertainty as a weighting factor, which addresses abrupt changes within image regions and enhances model stability.  
Our proposed patch-wise diffusion model, enhanced with uncertainty guidance, shows competitive performance in low-light image enhancement across multiple datasets, including LOLv1, LOLv2-real, and LOLv2-synthetic, achieving high scores in PSNR, SSIM, and LPIPS metrics. The model also performs well on additional datasets (DICM, LIME, MEF, NPE, and VV), with low NIQE scores that indicate effective preservation of natural image quality. With a moderate parameter count, this method balances performance and efficiency, making it applicable in resource-constrained environments. Overall, the approach addresses noise and irregular lighting challenges, providing an improvement in image enhancement with practical applicability.  
Although the proposed framework has shown significant improvements in low-light image enhancement, there remains room for further exploration. Future work could focus on extending the current model to support real-time applications and optimizing its computational efficiency for deployment on edge devices. Furthermore, for scenarios beyond the datasets used in this study, such as underground tunnels, rainy or foggy conditions, and dusty environments, future efforts will involve collecting low-light images in various complex scenarios. These efforts aim to enhance low-light image quality in challenging conditions and address practical issues encountered in real-world applications.  
Conceptualization, L.L. and Y.W.; methodology, L.L. and Y.W.; software, L.L. and Y.W. validation, L.L. and Y.W.; formal analysis, L.L. and Y.W.; data curation, Y.W.; writing--original draft preparation, L.L. and Y.W.; writing--review and editing, J.P. and L.L.; visualization, Y.W. and L.L.; supervision, J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study, due to the use of publicly available datasets.  
This study used publicly available dataset where informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The datasets used in this study, namely LOLv1, LOLv2, DICM, LIME, MEF, NPE and VV are all publicly available. The dataset links are as follows: LOLv1 can be accessed at [[https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website/]([https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website/), LOLv2 is available at [[https://pan.baidu.com/share/init?surl=7](https://pan.baidu.com/share/init?surl=7) Kta-6 GUUW22Q49D5 DhhWw&pwd=yixu]([https://pan.baidu.com/share/init?surl=7](https://pan.baidu.com/share/init?surl=7) Kta-6 GUUW22Q49D5 DhhWw&pwd=yixu) (code:yixu), DICM, LIME, MEF, NPE and VV can be found at [[https://pan.baidu.com/share/init?surl=FZ5](https://pan.baidu.com/share/init?surl=FZ5) HWT30 eghGuaAqqpJGaw&pwd=yixu]([https://pan.baidu.com/share/init?surl=FZ5](https://pan.baidu.com/share/init?surl=FZ5) HWT30 eghGuaAqqpJGaw&pwd=yixu) (code:yixu).  
Conflicts of Interest: Author [PERSON] is employed by the Material Company, State Grid Xinjiang Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.