# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 6 Conclusions  
The paper presents a framework that aims to enhance low-light images using the Retinex theory. The proposed framework is composed of two components: the Decom-Net and the Enhance-Net. The first one receives the low-light image and obtains the necessary reflection and illumination images. These images are then fed into the Enhance-Net to enhance the illumination images. The decomposed reflection images are fused with the corrected illumination images to obtain the final output. The proposed network model incorporates residual connectivity and attention mechanisms to reduce image detail loss. It also includes a positional pixel attention mechanism to ensure that the enhanced network does not lose image detail features in the image illumination. Additionally, a constant color loss is proposed to correct possible color bias in the enhanced image. The results of the experiments revealed that the suggested method performed well in capturing subjective visual effects and evaluating different metrics. Compared with RetinexNet, the proposed method increased the PSNR, SSIM, and LPIPS by 4.6%, 1.8%, and 10.8% in the full-reference evaluation index, respectively, and increased the NIQE without reference by an average of 17.3%. Although the proposed method has been shown to improve the color perception of images, further research is needed to develop it.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **MSE** & **NIQE** \\ \hline \(\lambda_{1}=\ \lambda_{2}=0.01\), \(\lambda_{3}=\ \lambda_{4}=0.01\) & 17.466 & _0.931_ & 1660.814 & **3.404** \\ \(\lambda_{1}=\ \lambda_{2}=0.01\), \(\lambda_{3}=\ \lambda_{4}=0.1\) & **17.590** & **0.928** & **1624.519** & 3.560 \\ \(\lambda_{1}=\ \lambda_{2}=0.01\), \(\lambda_{3}=\ \lambda_{4}=0.5\) & 17.281 & 0.927 & 1703.411 & **3.282** \\ \(\lambda_{1}=\ \lambda_{2}=0.1\), \(\lambda_{3}=\ \lambda_{4}=0.01\) & **17.470** & **0.932** & **1592.601** & **3.184** \\ \(\lambda_{1}=\ \lambda_{2}=0.1\), \(\lambda_{3}=\ \lambda_{3}=\ 0.1\) & 17.087 & 0.913 & 1850.028 & 3.931 \\ \(\lambda_{1}=\ \lambda_{2}=0.1\), \(\lambda_{3}=\ \lambda_{4}=0.5\) & 17.275 & 0.919 & 1711.196 & 3.752 \\ \(\lambda_{1}=\ \lambda_{2}=0.5\), \(\lambda_{3}=\ \lambda_{4}=0.01\) & **17.560** & 0.923 & **1611.588** & 3.461 \\ \(\lambda_{1}=\ \lambda_{2}=0.5\), \(\lambda_{3}=\ \lambda_{4}=0.1\) & 16.623 & 0.898 & 1919.451 & 3.930 \\ \(\lambda_{1}=\ \lambda_{2}=0.5\), \(\lambda_{3}=\ \lambda_{4}=0.5\) & 17.394 & 0.920 & 1720.743 & 3.406 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the evaluation were compared with the different parameters using the NIQE, MSE, PNSR, and SSIM metrics. The best results are highlighted in bold and underlined. The second-best results are in italics, and the third-best results are in bold.  
**Author Contributions:** Conceptualization, J.W.; methodology, J.W. and H.W.; software, J.W. and H.W.; validation, Y.S. and J.Y.; investigation, J.W. and Y.S.; writing--original draft preparation, J.W. and H.W.; writing--review and editing, Y.S. and J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available online: [[https://blog.csdn.net/u014546828/article/details/109256136](https://blog.csdn.net/u014546828/article/details/109256136)]([https://blog.csdn.net/u014546828/article/details/109256136](https://blog.csdn.net/u014546828/article/details/109256136)) (accessed on 24 November 2020).  
**Conflicts of Interest:** The authors declare no conflict of interest.