# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 4 Experiments
### Experimental Comparison
(**a**) Low-illumination image; (**b**) Retinex; (**c**) MSRCP; (**d**) MBLLEN; (**e**) LightenNet; (**f**) RetinexNet; (**g**) EnlightenGAN; (**h**) RRDNet; (**i**) R2 RNet; (**j**) DSLR; (**k**) ExCNet; (**l**) Zero-DCE; (**m**) Zero-ECD++; (**n**) our method. Table 1 shows the comparison results of the PSNR, SSIM, MSE, and LPIPS metrics for the LOL dataset's test set. From this, we can see that all brightness enhancement methods improved the performance metrics. The PSNR, SSIM, and MSE metrics of this method all scored high, with the PSNR metric leading the second place by 0.029 dB, the SSIM metric leading the second place by 0.002, and the MSE metric leading the second place by 39.287. The LPIPS and VIF metrics were also ranked in third and second place, respectively. This indicates that the enhancement effect of this method is closer to the real image than other methods. To evaluate the LIME, VV, and DICM datasets without reference evaluation indicators, we used the NIQE indicator as shown in Table 2. Our method ranks in the top three positions for both the LIME and DICM datasets. For a fairer comparison, we calculated the average scores of the three datasets and found that our proposed method outperforms most existing methods. The results of the paper's experimental procedures exhibited good scores in both the objective indicator and subjective visual evaluations. The proposed method's superiority was also highlighted. However, we also need to point out that there is still a slight color distortion phenomenon in comparison with some control images, although it is not very obvious, and this is still a direction for the further development of this work. In Table 3, we calculated the computational complexity based on the average runtime in the LOL test set. It can be observed that the runtime varies significantly among different methods, with EnlightenGAN being the fastest and RRDNet being slower. Compared to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **MSE** & **LPIPS** & **VIF** \\ \hline Input & 7.773 & 0.121 & 12613.620 & 0.560 & 0.266 \\ LightenNet [16] & 11.965 & 0.632 & 5661.574 & 0.359 & 0.704 \\ MBLLEN [15] & **17.655** & **0.930** & **1515.911** & **0.287** & **0.956** \\ RetinexNet [5] & 17.399 & 0.916 & 1565.746 & 0.363 & **0.963** \\ EnlightenGAN [17] & 17.539 & **0.919** & 1960.018 & 0.326 & 0.938 \\ DSLR [18] & 14.935 & 0.838 & 3679.389 & 0.403 & 0.862 \\ ExCNet [19] & 16.390 & 0.908 & 2159.039 & 0.327 & 0.935 \\ RRDNet [14] & 10.878 & 0.511 & 6966.992 & 0.336 & 0.642 \\ Zero-DCE [27] & 14.861 & 0.827 & 3281.716 & 0.335 & 0.868 \\ Zero-DCE++ [20] & 14.682 & 0.855 & 3207.455 & 0.340 & 0.882 \\ R2 RNet [6] & **18.179** & 0.892 & **1498.952** & **0.272** & 0.950 \\ Our method & **18.208** & **0.932** & **1459.655** & **0.318** & **0.959** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparisons were conducted on the LOL dataset using PSNR, SSIM, MSE, and LPIPS as evaluation 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. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **LIME** & **VV** & **DICM** & **Avg.** \\ \hline LightenNet [16] & 4.681 & 3.729 & 3.735 & 4.048 \\ MBLLEN [15] & 4.818 & 4.294 & 3.442 & 4.185 \\ RetinexNet [5] & 4.361 & 3.422 & 4.209 & 3.997 \\ EnlightenGAN [17] & **3.697** & **2.981** & 3.570 & **3.416** \\ DSLR [18] & 4.033 & 3.649 & 3.389 & 3.690 \\ ExCNet [19] & 4.162 & 3.783 & **3.039** & 3.661 \\ RRDNet [14] & 4.522 & 3.845 & 3.992 & 4.120 \\ Zero-DCE [27] & 3.928 & **3.217** & 3.716 & 3.620 \\ Zero-DCE++ [20] & 3.843 & 3.341 & **2.835** & **3.339** \\ R2 RNet [6] & **3.704** & **3.093** & 3.503 & 3.433 \\ Our method & **3.547** & 3.364 & **3.312** & **3.408** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the LIME, VV, and DICM datasets was conducted in terms of NIQE. The best results are highlighted in bold and underlined.