# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 6 Conclusions  
In this paper, we present a systematic enhancement of semantic-aware low-light image enhancement algorithms, focusing on the Semantic-aware Knowledge-Guided Framework (SKF). The key findings demonstrate that integrating semantic priors into the enhancement process--through modules such as Semantic Feature Enhancement, Semantic-Guided Color Histogram Loss, and Semantic-Guided Adversarial Loss--significantly improves both the visual quality and quantitative performance of low-light image restoration. It is notable that replacing the traditional RGB-only color constraint with multi-color space losses (incorporating RGB, LAB, and LCH) leads to more robust color fidelity and better perceptual realism, especially under challenging lighting conditions.  
Experimental results on benchmark datasets show that the SKF framework achieves state-of-the-art performance in terms of PSNR, SSIM, and perceptual metrics while also providing more natural and consistent color restoration. These improvements highlight the importance of semantic information for guiding both global and local enhancement, reducing color distortion, and preserving structural details.  
In summary, the SKF framework marks a significant advancement in low-light image enhancement by systematically leveraging semantic knowledge. In future work, further exploration into lightweight architectures, real-time inference, and broader semantic integration could extend the applicability of SKF to a wider range of real-world scenarios and devices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**LOL Dataset**} & \multicolumn{4}{c}{**LOL-v2 Dataset**} \\ \cline{2-9}  & **PSNR** & **SSIM** & **LPIPS** & **LRC PSNR** & **PSNR** & **SSIM** & **LPIPS** & **LRC PSNR** \\ \hline LLFlow & 27.476 & 0.942 & 0.078 & 6.502 & 27.524 & 0.91 & 0.111 & 7.787 \\ \hline LLFlow + SE & 28.121 & 0.942 & 0.074 & 6.502 & 27.979 & 0.91 & 0.101 & 7.778 \\ \hline LLFlow + SE + SCH & 29.901 & 0.944 & 0.068 & 6.503 & 28.805 & 0.911 & 0.093 & 7.766 \\ \hline LLFlow + SE + SCH + SA (LLFlow-SKF) & 30.804 & 0.945 & 0.064 & 6.503 & 29.194 & 0.912 & 0.093 & 7.755 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on different components on LLFlow-SKF.  
**Author Contributions:** Conceptualization, B.J. and N.Y.; Methodology, B.J., X.W., N.Y. and Q.W.; Software, B.J. and Q.W.; Resources, B.J.; Data curation, X.W.; Writing--original draft, X.W., N.Y., Y.L. and X.C.; Writing--review & editing, Y.L. and X.C.; Project administration, Q.W.; Funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 61907025).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: Available online: [[https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement](https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement)]([https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement](https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement)) (accessed on 26 February 2025).  
**Acknowledgments:** The authors would like to thank all the anonymous reviewers for their valuable suggestions to improve this work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.