## 5 Conclusions
### Future Research Directions  
Based on our findings and limitations, we identify several promising directions.  
* Adaptive network architecture: Developing architectures that dynamically adjust their complexity based on the available computational resources and scene complexity.
* Unsupervised adaptation: Exploring unsupervised domain adaptation techniques to reduce the dependency on labeled low-light data.
* Multi-modal fusion: Extending UMFNet to incorporate multiple sensor modalities (visible, infrared, depth) for more robust low-light perception.
* Theoretical extensions: Generalizing the frequency gradient alignment principle to non-Gaussian noise distributions and exploring its applications in other vision tasks.
* Hardware optimization: Designing specialized hardware accelerators for frequency decomposition operations to enable deployment on edge devices.  
**Author Contributions:** Conceptualization, S.G. and Z.M.; methodology, S.G.; software, S.G.; validation, S.G. and X.L.; formal analysis, S.G.; investigation, S.G.; resources, Z.M.; data curation, S.G.; writing--original draft preparation, S.G.; writing--review and editing, S.G. and X.L.; visualization, S.G.; supervision, Z.M. and X.L.; project administration, Z.M.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding. The APC was funded by the authors.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These datasets can be found here: ExDark dataset ([[https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/)]([https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/)), accessed on 30 March 2025), DarkFace dataset ([[https://flyywh.github.io/CVPRW2019](https://flyywh.github.io/CVPRW2019) LowLight/]([https://flyywh.github.io/CVPRW2019](https://flyywh.github.io/CVPRW2019) LowLight/), accessed on 30 March 2025), LOL dataset ([[https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website/]([https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website/), accessed on 30 March 2025), FLIR ADAS dataset ([[https://www.flir.com/oem/adas/adas-dataset-form/](https://www.flir.com/oem/adas/adas-dataset-form/)]([https://www.flir.com/oem/adas/adas-dataset-form/](https://www.flir.com/oem/adas/adas-dataset-form/)), accessed on 30 March 2025), and BDD100K-Night dataset ([[https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)]([https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)), accessed on 30 March 2025).  
**Acknowledgments:** The first author would like to thank the members of the Computer Vision research group of Electronic Information for their helpful discussions and suggestions throughout this research. The authors also appreciate the technical support provided by [PERSON].  
**Conflicts of Interest:** The authors declare no conflicts of interest.