# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 5 Conclusions  
This paper proposes a low-light image enhancement method called LACCNet that combines luminance adjustment and color correction while solving low-light problems, including uneven brightness, color distortion, and blurred content. We introduce the the non-overlapping window attention mechanism and the brightness adjustment curve to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modules** & **Networks** & **PSNR** & **SSIM** & **LPIPS** & **NIQE** \\ \hline \multirow{4}{*}{LAM} & LACCNet & \multirow{2}{*}{23.50} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.41} & \multirow{2}{*}{5.71} \\  & (w/o LAM) & & & & \\ \cline{1-1} \cline{3-6}  & LACCNet & \multirow{2}{*}{25.96} & \multirow{2}{*}{0.81} & \multirow{2}{*}{0.36} & \multirow{2}{*}{5.09} \\  & (w/o ALAM) & & & & \\ \cline{1-1} \cline{3-6}  & LACCNet & \multirow{2}{*}{24.75} & \multirow{2}{*}{0.79} & \multirow{2}{*}{0.39} & \multirow{2}{*}{5.38} \\  & (w/o CLAM) & & & & \\ \hline \multirow{3}{*}{CCM} & LACCNet & \multirow{3}{*}{23.19} & \multirow{3}{*}{0.75} & \multirow{3}{*}{0.29} & \multirow{3}{*}{4.77} \\  & (w/o CCM) & & & & \\ \hline \multirow{3}{*}{Color Loss} & LACCNet & \multirow{3}{*}{23.32} & \multirow{3}{*}{0.69} & \multirow{3}{*}{0.35} & \multirow{3}{*}{4.92} \\  & (w/o Color Loss) & & & & \\ \cline{1-1} \cline{3-6}  & LACCNet & \multirow{2}{*}{26.73} & \multirow{2}{*}{0.84} & \multirow{2}{*}{0.20} & \multirow{2}{*}{4.69} \\ \hline \hline \end{tabular}
\end{table}
Table 4: The objective evaluation results of LACCNet’s ablation experiments on the LOL-Blur test set.  
Figure 9: Comparison chart of ablation experiment results of color loss function on the LOL-V2-syn dataset.  
formulate a new Luminance Adjustment Module, called LAM. Then, we design a CCM that utilizes the attention mechanism to simulate and learn the color correction matrix to achieve color authenticity restoration. Finally, we introduce a novel color loss function to further improve the color similarity between the enhanced image and the ground truth. Extensive quantitative and qualitative experiments show that our proposed LACCNet dramatically outperforms other SOTA methods on six public datasets.  
The authors confirm their contribution to the paper as follows: study conception and design: N.Z., Y.C., X.H., C.L., R.G. and S.M.; data collection: N.Z. and X.H.; analysis and interpretation of results: X.H. and N.Z.; draft manuscript preparation: Y.C. and X.H. All authors reviewed the results and approved the final version of the manuscript.  
This research was supported by the project (JGKFKT2304) of the Key Laboratory of Convergent Media and Intelligent Technology of the Ministry of Education, Communication University of China, and the project \"Research on Key Technologies and Standards for Virtual Shooting Production\" (BKY202040230) of the Academy of Broadcasting Science, National Radio and Television Administration of China.  
Not applicable.  
The data presented in this study are available in LOL-Blur [25], LOL-V1 [9], LOL-V2 [26], LSRW [27], SICE [28], DICM [29], and MEF [30].  
The authors declare no conflicts of interest.