# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 4 Experiment and Results Analysis
### Experimental Comparison  
To reflect the effect of this algorithm, we compare the proposed approach with the traditional image enhancement algorithm and the latest image enhancement algorithm. Table 1 and Figure 2 show the comparison results between the classical low-illumination image-processing methods and our proposed methods on LOL datasets. The data show that our method is superior to other traditional image enhancement algorithms in terms of quality evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **UQI** \\ \hline HE & 27.927 & 0.898 & 0.783 \\ Retinex [10] & 27.562 & 0.616 & 0.674 \\ MSR [15] & 27.439 & 0.633 & 0.728 \\ MSRCP [17] & 27.994 & 0.624 & 0.634 \\ LIME [14] & 27.825 & 0.878 & 0.776 \\ RetinexNet [21] & 27.869 & 0.894 & 0.794 \\ Ours & **28.534** & **0.988** & **0.868** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of the results of each method. The bold numbers indicate the best performance.  
Figure 3 shows the indoor low illumination image and the enhanced effect picture. As you can see from the zoom in detail, indoor images were enhanced by HE, Retinex, and MSR, resulting in white, obvious distortion, and serious loss of image details. After processing with the RetinexNet algorithm, the overall brightness of the image is improved, but the contrast is reduced, the overall image becomes blurred, and the visual effect is reduced. LIME enhancement effect is dark on the whole and not obvious in dark areas.  
Figure 4 shows the enhanced effect of outdoor low-illumination images. It can be seen from the detailed amplification effect display that the overall enhancement effect of Retinex and MSR is white, and the car light distortion is obvious; the enhancement effect of HE is relatively natural, but the image is noisy; the enhancement effect is not obvious in dark areas of MSRCP and LIME, and details are lost; RetinexNet Enhances the overall relative blur of the image.  
The mean opinion score (MOS) test is a commonly used subjective image quality assessment method; human raters were asked to assign perceptual quality scores to test images. Usually, the score ranges from 1 (poor) to 5 (good). The final MOS is calculated as the arithmetic mean for all grades.  
Figure 3: Contrast of indoor image enhancement effect. (**a**) Original image; (**b**) HE; (**c**) Retinex; (**d**) MSR; (**e**) MSRCP; (**f**) LIME; (**g**) RetinexNet; (**h**) Ours.  
Figure 2: Enhanced effect of low illumination image. (**a**) Low illumination image; (**b**) HE; (**c**) Retinex; (**d**) MSR; (**c**) MSRCP; (**f**) LIME; (**g**) RetinexNet; (**h**) Ours; (**I**) Ground Truth.  
The expression of MOS is as follows:  
\[\text{MOS}=\frac{\sum_{n=1}^{N}R_{n}}{N} \tag{13}\]  
where \(R\) represents the number of evaluators' satisfaction scores for the image, and \(N\) represents the total number of evaluators. We selected the results of 100 questionnaires, and the average opinion score is shown in Table 2.  
As can be seen from Table 2, our method gets the highest score in indoor image enhancement effect. As for outdoor image enhancement, our score is tied in the first place with HE, but the score is still higher when compared with the others.